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An experimental investigation of the integration of smart building components with building information model (BIM) 36

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An experimental investigation of the integration of smart bu...

36th International Symposium on Automation and Robotics in Construction, ISARC 2019

作者： Afsari, K. Florez, L. Maneke, E. Afkhamiaghda, M. School of Construction Management Technology Purdue University United States Department of Architecture and Built Environment Northumbria University United Kingdom Department of Computer Graphics Technology Purdue University United States

Building Information Modeling (BIM) is a methodology to digitally represent all the physical and functional characteristics of a building. Importantly, in smart buildings smart components that are enabled with sensing and actuation need to be modeled accurately within the BIM model. This data representation needs to include multiple status of the smart component based on their performance to guide the design and construction process. However, currently there is not a clear methodology or guideline on how to embed smart components in the BIM model. Visualization techniques have been developed based on CAD technology to integrate smart components in the building model but these techniques have not been applied to BIM environment. To accurately model smart components, the component must be more than a single status representation and must contain complete and accurate dynamic data of the smart component. In this research, data properties, visualization techniques, and categorization of smart components is investigated. Then, through an experimental investigation, nine smart components across five building disciplines are modeled and embedded in a BIM model of a smart space. The model includes parameters that facilitate the data representation of the smart components. Data properties, data organization, and simulation of the smart component within the building model is explained. Challenges and future research is discussed. © 2019 International Association for Automation and Robotics in Construction I.A.A.R.C. All rights reserved.

关键词： Visualization

Assessing Trustworthy AI in Times of COVID-19: Deep Learning for Predicting a Multiregional Score Conveying the Degree of Lung Compromise in COVID-19 Patients

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IEEE Transactions on Technology and Society

IEEE Transactions on technology and Society 2022年第4期3卷 272-289页

作者： Allahabadi, Himanshi Amann, Julia Balot, Isabelle Beretta, Andrea Binkley, Charles Bozenhard, Jonas Bruneault, Frederick Brusseau, James Candemir, Sema Cappellini, Luca Alessandro Chakraborty, Subrata Cherciu, Nicoleta Cociancig, Christina Coffee, Megan Ek, Irene Espinosa-Leal, Leonardo Farina, Davide Fieux-Castagnet, Genevieve Frauenfelder, Thomas Gallucci, Alessio Giuliani, Guya Golda, Adam Van Halem, Irmhild Hildt, Elisabeth Holm, Sune Kararigas, Georgios Krier, Sebastien A. Kuhne, Ulrich Lizzi, Francesca Madai, Vince I. Markus, Aniek F. Masis, Serg Mathez, Emilie Wiinblad Mureddu, Francesco Neri, Emanuele Osika, Walter Ozols, Matiss Panigutti, Cecilia Parent, Brendan Pratesi, Francesca Moreno-Sanchez, Pedro A. Sartor, Giovanni Savardi, Mattia Signoroni, Alberto Sormunen, Hanna-Maria Spezzatti, Andy Srivastava, Adarsh Stephansen, Annette F. Theng, Lau Bee Tithi, Jesmin Jahan Tuominen, Jarno Umbrello, Steven Vaccher, Filippo Vetter, Dennis Westerlund, Magnus Wurth, Renee Zicari, Roberto V. Ey Netherlands Enterprise Intelligence Department Amsterdam1083 HP Netherlands Eth Zurich Health Ethics and Policy Lab Department of Health Sciences and Technology Zürich8092 Switzerland Center for Diplomatic and Strategic Studies Postgraduate Studies in Diplomacy and International Relations Paris75015 France Pisa56124 Italy Hackensack Meridian Health Bioethics Center EdisonNJ08820 United States University of Oxford Faculty of Philosophy OxfordOX2 6GG United Kingdom Collège André- Laurendeau Philosophie Department MontrealQCH8N 2J4 Canada Université du Québec À Montréal École des Médias MontrealQCH2L 2C4 Canada Pace University Philosophy Department New YorkNY10038 United States The Ohio State University Wexner Medical Center Department of Radiology ColumbusOH43210 United States Humanitas Research Hospital Department of Radiology Milan20089 Italy Humanitas University Department of Biomedical Sciences Milan20089 Italy University of New England Faculty of Science Agriculture Business and Law ArmidaleNSW2351 Australia University of Technology Sydney Faculty of Engineering and Information Technology SydneyNSW2007 Australia Scuola Superiore Sant'Anna European Centre of Excellence on the Regulation of Robotics and Ai Pisa56127 Italy University of Bremen Group of Computer Architecture Bremen28359 Germany New York University Grossman School of Medicine Division of Infectious Diseases and Immunology Department of Medicine New YorkNY10016 United States Digital Institute Ai Research Section Stockholm16731 Sweden Arcada University of Applied Sciences Department of Business Management and Analytics Helsinki00550 Finland University of Brescia Radiological Sciences and Public Health Department of Medical and Surgical Specialties Brescia25121 Italy Sncf Reseau Sa Ethique Groupe La Plaine93418 France Institute of Diagnostic and Interventional Radiology University Hospital Zurich Zürich8091 Switzerland Eindhoven University of Tech

This article's main contributions are twofold: 1) to demonstrate how to apply the general European Union's High-Level Expert Group's (EU HLEG) guidelines for trustworthy AI in practice for the domain of healthcare and 2) to investigate the research question of what does 'trustworthy AI' mean at the time of the COVID-19 pandemic. To this end, we present the results of a post-hoc self-assessment to evaluate the trustworthiness of an AI system for predicting a multiregional score conveying the degree of lung compromise in COVID-19 patients, developed and verified by an interdisciplinary team with members from academia, public hospitals, and industry in time of pandemic. The AI system aims to help radiologists to estimate and communicate the severity of damage in a patient's lung from Chest X-rays. It has been experimentally deployed in the radiology department of the ASST Spedali Civili clinic in Brescia, Italy, since December 2020 during pandemic time. The methodology we have applied for our post-hoc assessment, called Z-Inspection®, uses sociotechnical scenarios to identify ethical, technical, and domain-specific issues in the use of the AI system in the context of the pandemic. © 2020 IEEE.

关键词： COVID-19

Tackling the muon identification in water Cherenkov detectors problem for the future Southern Wide-field Gamma-ray Observatory by means of Machine Learning

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arXiv 2021年

作者： González, B.S. Conceição, R. Pimenta, M. Tomé, B. Guillén, A. - Lisbon Av. Prof. Gama Pinto 2 Lisbon1649-003 Portugal Universidade de Lisboa Av. Rovisco Pais 1 Lisbon1049-001 Portugal Computer Architecture and Technology Department University of Granada Granada Spain

This paper presents several approaches to deal with the problem of identifying muons in a water Cherenkov detector with a reduced water volume and 4 PMTs. Different perspectives of information representation are used and new features are engineered using the specific domain knowledge. As results show, these new features, in combination with the convolutional layers, are able to achieve a good performance avoiding overfitting and being able to generalise properly for the test set. The results also prove that the combination of state-of-the-art Machine Learning analysis techniques and water Cherenkov detectors with low water depth can be used to efficiently identify muons, which may lead to huge investment savings due to the reduction of the amount of water needed at high altitudes. This achievement can be used in further research to be able to discriminate between gamma and hadron induced showers using muons as discriminant. © 2021, CC BY-NC-SA.

关键词： Cosmic rays

A Cost Model for Data Discovery in Large-Scale IoT Networks of Smart Cities

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A Cost Model for Data Discovery in Large-Scale IoT Networks ...

International Conference on Mobile Data Management

作者： Torbjørn Kirkevik Soltvedt Amir Sinaeepourfard Dirk Ahlers Norwegian University of Science and Technology (NTNU) Trondheim Norway Department of Computer Science Norwegian University of Science and Technology (NTNU) Trondheim Norway Department of Architecture and Planning Norwegian University of Science and Technology (NTNU) Trondheim Norway

ISBN: (数字)9781728146638

ISBN: (纸本)9781728146645

A smart city with huge numbers of physical (e.g., sensors and actuators) and non-physical (e.g., external databases) data sources will continuously produce high amounts of massive city-data. Distributed data storage across the city may store the produced city-data. City managers through different update mechanisms may send the produced city-data from distributed data storage to centralized data storage (e.g., Cloud data storage). Hence, the data discovery issues are in a vital position in the smart city concepts because the produced city-data may exist in different data storage platforms from distributed to centralized data. In this paper, we will first present our proposed Distributed-to-Centralized Information and Communications technology (D2C-ICT) architecture for the Zero Emission Neighborhoods (ZEN) center. This proposed D2C-ICT architecture can provide multiple facilities from the joined benefits of distributed and centralized technologies in smart cities. Second, we will show how the Multi-Attribute Utility Theory (MAUT) cost model can be beneficial to find the appropriate data for building city services across the different storage platforms on the city scale as well as can be applied in the ZEN center and its pilots.

关键词： Conferences

Rubik: A hierarchical architecture for efficient graph learning

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arXiv 2020年

作者： Chen, Xiaobing Wang, Yuke Xie, Xinfeng Hu, Xing Basak, Abanti Liang, Ling Yan, Mingyu Deng, Lei Ding, Yufei Du, Zidong Chen, Yunji Xie, Yuan State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing100190 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Department of Computer Science University of California Santa Barbara United States Department of Electrical and Computer Engineering University of California Santa Barbara United States

Graph convolutional network (GCN) emerges as a promising direction to learn the inductive representation in graph data commonly used in widespread applications, such as E-commerce, social networks, and knowledge graphs. However, learning from graphs is non-trivial because of its mixed computation model involving both graph analytics and neural network computing. To this end, we decompose the GCN learning into two hierarchical paradigms: graph-level and node-level computing. Such a hierarchical paradigm facilitates the software and hardware accelerations for GCN learning. We propose a lightweight graph reordering methodology, incorporated with a GCN accelerator architecture that equips a customized cache design to fully utilize the graph-level data reuse. We also propose a mapping methodology aware of data reuse and task-level parallelism to handle various graphs inputs effectively. Results show that Rubik accelerator design improves energy efficiency by 26.3x to 1375.2x than GPU platforms across different datasets and GCN models. Copyright © 2020, The Authors. All rights reserved.

关键词： Graph neural networks

A testbed for investigation of selective laser melting at elevated atmospheric pressure

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arXiv 2021年

作者： Griggs, David A. Gibbs, Jonathan S. Baker, Stuart P. Penny, Ryan W. Feldmann, Martin C. Hart, A. John Department of Mechanical Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Department of Naval Architecture and Ocean Engineering U.S. Naval Academy AnnapolisMD United States Materials and Manufacturing Directorate Air Force Research Laboratory DaytonOH United States

Metal additive manufacturing (AM) by laser powder bed fusion (L-PBF) builds upon fundamentals established in the field of laser welding which include the influence of gas and plume dynamics on weld depth and quality. L-PBF demands a thorough investigation of the complex thermophysical phenomena that occur where the laser interacts with the metal powder bed. In particular, melt pool turbulence and evaporation are influenced by the ambient gas chemistry and pressure. This paper presents the design and validation of high pressure laser melting (HPLM) testbed;this accommodates bare metal plate samples as well as manually-coated single powder layers, and operates at up to 300 psig. The open architecture of this testbed allows for full control of all relevant laser parameters in addition to ambient gas pressure and gas flow over the build area. Representative melt tracks and rasters on bare plate and powder are examined in order to validate system performance, and preliminary analysis concludes that pressure has a significant impact on melt pool aspect ratio. The HPLM system thus enables careful study pressure effects on processing of common L-PBF materials, and can be applied in the future to materials that are challenging to process under ambient pressure, such as those with high vapor pressures. © 2021, CC BY.

关键词： Lakes

The Mercedes water Cherenkov detector

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arXiv 2022年

作者： Assis, P. Bakalová, A. Barres De Almeida, U. Brogueira, P. Conceição, R. De Angelis, A. Gibilisco, L. González, B.S. Guillén, A. La Mura, G. Mendes, L.M.D. Mendes, L.F. Pimenta, M. Shellard, R.C. Tomé, B. Vícha, J. Laboratório de Instrumentac ão e Física Experimental de Partículas Lisbon Portugal Universidade de Lisboa Lisbon Portugal Institute of Physics The Czech Academy of Sciences Prague Czech Republic Rio de Janeiro Brazil Università di Padova INFN INAF PadovaI-35131 Italy Computer Architecture and Technology Department University of Granada Granada Spain

The concept of a small, single-layer water Cherenkov detector, with three photomultiplier tubes (PMTs), placed at its bottom in a 120° star configuration (Mercedes Water Cherenkov Detector) is presen-ted. The PMTs are placed near the lateral walls of the stations with an adjustable inclination and may be in-stalled inside or outside the water volume. To illustrate the technical viability of this concept and obtain a first-order estimation of its cost, an engineering design was elaborated. The sensitivity of these stations to low energy Extensive Air Shower (EAS) electrons, photons and muons is discussed, both in compact and sparse array configurations. It is shown that the analysis of the intensity and time patterns of the PMT signals, using machine learning techniques, enables the tagging of muons, achieving an excellent gamma/hadron discrimination for TeV showers. This concept minimises the station production and maintenance costs, allowing for a highly exible and fast installation. Mercedes Water Cherenkov Detectors (WCDs) are thus well-suited for use in high-altitude large gamma-ray observatories covering an extended energy range from the low energies, closing the gap between satellite and ground-based measurements, to very high energy regions, beyond the PeV scale. Copyright © 2022, The Authors. All rights reserved.

关键词： Tellurium compounds

Impact of Large-Scale Correlated Failures on Multilevel Virtualized Networks

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Impact of Large-Scale Correlated Failures on Multilevel Virt...

IEEE Workshop on High Performance Switching and Routing

作者： Max G. Medina Mohammed J.F. Alenazi Egemen K. Çetinkaya Department of Electrical & Computer Eng. Missouri Univ. of Science and Technology Rolla MO USA Department of Computer Eng. King Saud University Riyadh Saudi Arabia Technology Architecture and Planning Verizon Basking Ridge NJ USA

ISBN: (数字)9781728148465

ISBN: (纸本)9781728148472

Communication network architectures have been evolving with increased programmability and virtualization of physical infrastructures. While this evolution enables a faster introduction of new services and efficient utilization of resources, the complexities and interdependencies among the physical and virtual infrastructures, as well as the control and management planes, hinder a complete understanding of the system at hand. It is imperative, therefore, to model and study these evolving virtualized infrastructures. We propose a multilevel virtualized network embedding model and study its performance against correlated failures. Our analysis of multiple synthetic network models identified that grid-like infrastructures capture the realistic Internet2 structure the best.

关键词： Analytical models Topology Measurement Resilience Network architecture Cloud computing

Non-convex potential games for finding global solutions to sensor network localization

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arXiv 2023年

作者： Xu, Gehui Chen, Guanpu Hong, Yiguang Fidan, Baris Parisini, Thomas Johansson, Karl H. Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Beijing China Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm100 44 Sweden Department of Control Science and Engineering Tongji University Shanghai201804 China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai201210 China Department of Mechanical and Mechatronics Engineering University of Waterloo WaterlooONN2L 3G1 Canada Department of Electrical and Electronic Engineering Imperial College London LondonSW7 2AZ United Kingdom Department of Engineering and Architecture University of Trieste Trieste34127 Italy

Sensor network localization (SNL) problems require determining the physical coordinates of all sensors in a network. This process relies on the global coordinates of anchors and the available measurements between non-anchor and anchor nodes. Attributed to the intrinsic non-convexity, obtaining a globally optimal solution to SNL is challenging, as well as implementing corresponding algorithms. In this paper, we formulate a non-convex multi-player potential game for a generic SNL problem to investigate the identification condition of the global Nash equilibrium (NE) therein, where the global NE represents the global solution of SNL. We employ canonical duality theory to transform the non-convex game into a complementary dual problem. Then we develop a conjugation-based algorithm to compute the stationary points of the complementary dual problem. On this basis, we show an identification condition of the global NE: the stationary point of the proposed algorithm satisfies a duality relation. Finally, simulation results are provided to validate the effectiveness of the theoretical results. Copyright © 2023, The Authors. All rights reserved.

关键词： Sensor networks