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Detection and Localization of Manipulated Smart Meters Using Super State Hidden Markov Models

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Detection and Localization of Manipulated Smart Meters Using...

IEEE International Conference on Communications, Control, and Computing Technologies for Smart Gridss

作者： Raoul Gabriel Jorg Matthes Hubert B. Keller Veit Hagenmeyer Research & Development ci-Tec GmbH Institute for Automation and Applied Informatics (IAI) Karlsruhe Institute of Technology

ISBN: (纸本)9781538681008

Manipulated energy metering devices lead not only to severe economic damages, they also can directly lead to unfortunate decisions concerning stable grid operation, especially for Smart Grids of the future with less and less rotating inertia and more and more volatile infeed of renewables. For this reason, we propose in the present paper a new manipulation detection method using Super State Hidden Markov Models (SSHMMs) in combination with an additional central metering device. The proposed new method does not only allow the detection of the manipulation of the Smart Meters, but additionally infers which meters are most likely manipulated. Thus, the new method is able to detect attacks on the grid and to pinpoint the manipulated devices, enabling the network provider to initiate further counteractions. The accuracy and effectiveness of the new method are shown by its application to a distribution grid example. It is ready-to-use for network providers of classical distribution grids.

关键词： network provider Power grids Smart meters Smart grids Hidden Markov models Renewable energy new methods grids

Lockdown effects on air quality in megacities during the first and second waves of COVID-19 pandemic

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arXiv 2020年

作者： Giri, Aswin J. Schäfer, Benjamin Verma, Rulan He, Hankun Nagendra, S.M. Shiva Khare, Mukesh Beck, Christian Indian Institute of Technology Madras India Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Eggenstein-Leopoldshafen76344 Germany School of Mathematical Sciences Queen Mary University of London United Kingdom Indian Institute of Technology Delhi India The Alan Turing Institute 96 Euston Road LondonNW1 2DB United Kingdom

Air pollution is among the highest contributors to mortality worldwide, especially in urban areas. During spring 2020, many countries enacted social distancing measures in order to slow down the ongoing COVID-19 pandemic. A particularly drastic measure, the "lockdown", urged people to stay at home and thereby prevent new COVID-19 infections during the first (2020) and second wave (2021) of the pandemic. In turn, it also reduced traffic and industrial activities. But how much did these lockdown measures improve air quality in large cities, and are there differences in how air quality was affected? Here, we analyse data from two megacities: London as an example for Europe and Delhi as an example for Asia. We consider data during first and second wave lockdowns and compare them to 2019 values. Overall, we find a reduction in almost all air pollutants with intriguing differences between the two cities except Delhi in 2021. In London, despite smaller average concentrations, we still observe high-pollutant states and an increased tendency towards extreme events (a higher kurtosis of the probability density during lockdown) during 2020 and low pollution levels during 2021. For Delhi, we observe a much stronger decrease of pollution concentrations, including high pollution states during 2020 and higher pollution levels in 2021. These results could help to design policies to improve long-term air quality in megacities. Copyright © 2020, The Authors. All rights reserved.

关键词： Air quality

Measurement of the 2νββ Decay Rate and Spectral Shape of Mo100 from the CUPID-Mo Experiment

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Physical Review Letters 2023年第16期131卷 162501-162501页

作者： C. Augier A. S. Barabash F. Bellini G. Benato M. Beretta L. Bergé J. Billard Yu. A. Borovlev L. Cardani N. Casali A. Cazes E. Celi M. Chapellier D. Chiesa I. Dafinei F. A. Danevich M. De Jesus T. Dixon L. Dumoulin K. Eitel F. Ferri B. K. Fujikawa J. Gascon L. Gironi A. Giuliani V. D. Grigorieva M. Gros D. L. Helis H. Z. Huang R. Huang L. Imbert J. Johnston A. Juillard H. Khalife M. Kleifges V. V. Kobychev Yu. G. Kolomensky S. I. Konovalov J. Kotila P. Loaiza L. Ma E. P. Makarov P. de Marcillac R. Mariam L. Marini S. Marnieros X.-F. Navick C. Nones E. B. Norman E. Olivieri J. L. Ouellet L. Pagnanini L. Pattavina B. Paul M. Pavan H. Peng G. Pessina S. Pirro D. V. Poda O. G. Polischuk S. Pozzi E. Previtali Th. Redon A. Rojas S. Rozov V. Sanglard J. A. Scarpaci B. Schmidt Y. Shen V. N. Shlegel F. Šimkovic V. Singh C. Tomei V. I. Tretyak V. I. Umatov L. Vagneron M. Velázquez B. Ware B. Welliver L. Winslow M. Xue E. Yakushev M. Zarytskyy A. S. Zolotarova Univ Lyon Université Lyon 1 CNRS/IN2P3 IP2I-Lyon F-69622 Villeurbanne France National Research Centre “Kurchatov Institute ” Kurchatov Complex of Theoretical and Experimental Physics 117218 Moscow Russia Dipartimento di Fisica Sapienza Università di Roma Piazzale Aldo Moro 2 I-00185 Rome Italy INFN Sezione di Roma P.le Aldo Moro 2 I-00185 Rome Italy INFN Laboratori Nazionali del Gran Sasso I-67100 Assergi (AQ) Italy INFN Gran Sasso Science Institute I-67100 L’Aquila Italy University of California Berkeley California 94720 USA Université Paris-Saclay CNRS/IN2P3 IJCLab 91405 Orsay France Nikolaev Institute of Inorganic Chemistry 630090 Novosibirsk Russia Dipartimento di Fisica Università di Milano-Bicocca I-20126 Milano Italy INFN Sezione di Milano-Bicocca I-20126 Milano Italy Institute for Nuclear Research of NASU 03028 Kyiv Ukraine INFN Sezione di Roma Tor Vergata Via della Ricerca Scientifica 1 I-00133 Rome Italy IRFU CEA Université Paris-Saclay F-91191 Gif-sur-Yvette France Karlsruhe Institute of Technology Institute for Astroparticle Physics 76021 Karlsruhe Germany Lawrence Berkeley National Laboratory Berkeley California 94720 USA Key Laboratory of Nuclear Physics and Ion-beam Application (MOE) Fudan University Shanghai 200433 People’s Republic of China Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Karlsruhe Institute of Technology Institute for Data Processing and Electronics 76021 Karlsruhe Germany National Research Centre Kurchatov Institute Institute of Theoretical and Experimental Physics 117218 Moscow Russia Department of Physics University of Jyväskylä P.O. Box 35 FI-40014 Jyväskylä Finland Finnish Institute for Educational Research University of Jyväskylä P.O. Box 35 FI-40014 Jyvaäskylä Finland Center for Theoretical Physics Sloane Physics Laboratory Yale University New Haven Connecticut 06520-8120 USA Physik Department Technische Universität München Garching D-85748 Germany Department of Modern P

Neutrinoless double beta decay (0νββ) is a yet unobserved nuclear process that would demonstrate Lepton number violation, a clear evidence of beyond standard model physics. The process two neutrino double beta decay (2νββ) is allowed by the standard model and has been measured in numerous experiments. In this Letter, we report a measurement of 2νββ decay half-life of Mo100 to the ground state of Ru100 of [7.07±0.02(stat)±0.11(syst)]×1018 yr by the CUPID-Mo experiment. With a relative precision of ±1.6% this is the most precise measurement to date of a 2νββ decay rate in Mo100. In addition, we constrain higher-order corrections to the spectral shape, which provides complementary nuclear structure information. We report a novel measurement of the shape factor ξ3,1=0.45±0.03(stat)±0.05(syst) based on a constraint on the ratio of higher-order terms from theory, which can be reliably calculated. This is compared to theoretical predictions for different nuclear models. We also extract the first value for the effective axial vector coupling constant obtained from a spectral shape study of 2νββ decay.

关键词： Double beta decay Neutrinoless double beta decay Nuclear structure & decays

Towards automatic embryo staging in 3d+t microscopy images using convolutional neural networks and pointnets

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arXiv 2019年

作者： Traub, Manuel Stegmaier, Johannes Institute of Imaging and Computer Vision RWTH Aachen University Aachen Germany Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Karlsruhe Germany

Automatic analyses and comparisons of different stages of embryonic development largely depend on a highly accurate spatio-temporal alignment of the investigated data sets. In this contribution, we compare multiple approaches to perform automatic staging of developing embryos that were imaged with time-resolved 3D light-sheet microscopy. The methods comprise image-based convolutional neural networks as well as an approach based on the PointNet architecture that directly operates on 3D point clouds of detected cell nuclei centroids. The proof-of-concept experiments with four wild-type zebrafish embryos render both approaches suitable for automatic staging with average deviations of 0.45 − 0.57 hours. Copyright © 2019, The Authors. All rights reserved.

关键词： Biology

Modeling of Distributed Ledgers: Challenges and Future Perspectives

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Modeling of Distributed Ledgers: Challenges and Future Persp...

IEEE Conference on Commerce and Enterprise Computing, CEC

作者： Sergey Smetanin Aleksandr Ometov Niclas Kannengießer Benjamin Sturm Mikhail Komarov Ali Sunyaev Faculty of Business and Management National Research University Higher School of Economics Moscow Russia Faculty of Information Technology and Communication Sciences Tampere University Tampere Finland Institute of Applied Informatics and Formal Description Methods Karlsruhe Institute of Technology Karlsruhe Germany

ISBN: (数字)9781728199269

ISBN: (纸本)9781728199276

Interest in applications based on distributed ledger technology (DLT) is on the rise, with corporations worldwide shifting from simply exploring DLT's potential to creating productive business cases. However, despite the existence of numerous DLT applications, developers are still lacking proper tools and instruments for evaluating system behavior (e.g., performance) of their applications on different distributed ledgers before deployment. Since the behavior of such applications is highly dependent on the characteristics of the distributed ledger they are built upon and changing the distributed ledger after deployment of an application is difficult, selecting the wrong ledger can have severe negative consequences. To address the issue, we conducted an extensive literature review to identify and synthesize published modeling and simulation approaches for distributed ledgers. Based on the results, this paper also presents a research agenda to improve modeling of the system behavior of distributed ledgers and to support the development of distributed ledgers and viable DLT applications. In doing so, we facilitate informed decision-making for suitable modeling or simulation approach, which helps application developers to identify a suitable distributed ledger before implementing an application. In addition, our work contributes to science, as we provide a comprehensive overview and analysis of extant simulation and modeling approaches in DLT that accumulates the current state-of-the-art in the rapidly growing DLT field.

关键词： Analytical models Distributed databases Protocols Bitcoin Throughput

Open data base analysis of scaling and spatio-temporal properties of power grid frequencies

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arXiv 2020年

作者： Gorjão, Leonardo Rydin Jumar, Richard Maass, Heiko Hagenmeyer, Veit Yalcin, G. Cigdem Kruse, Johannes Timme, Marc Beck, Christian Witthaut, Dirk Schäfer, Benjamin Germany Institute for Theoretical Physics University of Cologne Germany Karlsruhe Institute of Technology Institute for Automation and Applied Informatics Germany Department of Physics Istanbul University Vezneciler34134 Turkey Institute for Theoretical Physics Technical University of Dresden Germany School of Mathematical Sciences Queen Mary University of London United Kingdom

The electrical energy system has attracted much attention from an increasingly diverse research community. Many theoretical predictions have been made, from scaling laws of fluctuations to propagation velocities of disturbances. However, to validate any theory, empirical data from large-scale power systems are necessary but are rarely shared openly. Here, we analyse an open data base of measurements of electric power grid frequencies across 17 locations in 12 synchronous areas on three continents. The power grid frequency is of particular interest, as it indicates the balance of supply and demand and carries information on deterministic, stochastic, and control influences. We perform a broad analysis of the recorded data, compare different synchronous areas and validate a previously conjectured scaling law. Furthermore, we show how fluctuations change from local independent oscillations to a homogeneous bulk behaviour. Overall, the presented open data base and analyses may constitute a step towards more shared, collaborative energy research. Copyright © 2020, The Authors. All rights reserved.

关键词： Open Data

JARVIS-Leaderboard: A Large Scale Benchmark of Materials Design Methods

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arXiv 2023年

作者： Choudhary, Kamal Wines, Daniel Garrity, Kevin F. Williams, Maureen Tavazza, Francesca Li, Kangming Hattrick-Simpers, Jason Gupta, Vishu Romero, Aldo H. Krogel, Jaron T. Saritas, Kayahan Fuhr, Addis Ganesh, Panchapakesan Kent, Paul R.C. Yan, Keqiang Lin, Yuchao Ji, Shuiwang Blaiszik, Ben Reiser, Patrick Friederich, Pascal Agrawal, Ankit Tiwary, Pratyush Beyerle, Eric Minch, Peter Rhone, Trevor David Takeuchi, Ichiro Wexler, Robert B. Mannodi-Kanakkithodi, Arun Ertekin, Elif Mishra, Avanish Mathew, Nithin Wood, Mitchell Rohskopf, Andrew Dale Wang, Shih-Han Achenie, Luke E.K. Xin, Hongliang Biacchi, Adam J. Material Measurement Laboratory National Institute of Standards and Technology MD20899 United States Department of Materials Science and Engineering University of Toronto 27 King’s College Cir TorontoON Canada Department of Electrical and Computer Engineering Northwestern University EvanstonIL60208 United States Lewis-Sigler Institute for Integrative Genomics Princeton University PrincetonNJ08544 United States Ludwig Institute for Cancer Research Princeton University PrincetonNJ08544 United States Department of Physics and Astronomy West Virginia University MorgantownWV26506 United States Materials Science and Technology Division Oak Ridge National Laboratory Oak RidgeTN37831 United States Center for Nanophase Materials Science Oak Ridge National Laboratory Oak RidgeTN37831 United States Computational Sciences and Engineering Division Oak Ridge National Laboratory Oak RidgeTN37831 United States Department of Computer Science and Engineering Texas A&M University College StationTX77843 United States Globus University of Chicago IL60637 United States Data Science Learning Division National Lab ArgonneIL60439 United States Institute of Nanotechnology Karlsruhe Institute of Technology Kaiserstraße 12 Karlsruhe76131 Germany Institute of Theoretical Informatics Karlsruhe Institute of Technology Kaiserstraße 12 Karlsruhe76131 Germany Department of Chemistry and Biochemistry Institute for Physical Science and Technology University of Maryland College ParkMD20742 United States Department of Physics Applied Physics and Astronomy Rensselaer Polytechnic Institute TroyNY12180 United States Department of Materials Science and Engineering University of Maryland College ParkMD20742 United States Department of Chemistry Institute of Materials Science and Engineering Washington University in St. Louis St. LouisMO63130 United States School of Materials Engineering Purdue University West LafayetteIN47907 United States Department of Mechan

Lack of rigorous reproducibility and validation are significant hurdles for scientific development across many fields. Materials science, in particular, encompasses a variety of experimental and theoretical approaches that require careful benchmarking. Leaderboard efforts have been developed previously to mitigate these issues. However, a comprehensive comparison and benchmarking on an integrated platform with multiple data modalities with perfect and defect materials data is still lacking. This work introduces JARVIS-Leaderboard, an open-source and community-driven platform that facilitates benchmarking and enhances reproducibility. The platform allows users to set up benchmarks with custom tasks and enables contributions in the form of dataset, code, and meta-data submissions. We cover the following materials design categories: Artificial Intelligence (AI), Electronic Structure (ES), Force-fields (FF), Quantum Computation (QC) and Experiments (EXP). For AI, we cover several types of input data, including atomic structures, atomistic images, spectra, and text. For ES, we consider multiple ES approaches, software packages, pseudopotentials, materials, and properties, comparing results to experiment. For FF, we compare multiple approaches for material property predictions. For QC, we benchmark Hamiltonian simulations using various quantum algorithms and circuits. Finally, for experiments, we use the inter-laboratory approach to establish benchmarks. There are 1281 contributions to 274 benchmarks using 152 methods with more than 8 million data-points, and the leaderboard is continuously expanding. The JARVIS-Leaderboard is available at the website: https://***/jarvis_leaderboard/ © 2023, CC BY.

关键词： Electronic structure

An Industry 4.0 framework for tooling production using metal additive manufacturing-based first-time-right smart manufacturing system

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Procedia CIRP 2020年 93卷 32-37页

作者： Mandaná Moshiri Amal Charles Ahmed Elkaseer Steffen Scholz Sankhya Mohanty Guido Tosello Department of Mechanical Engineering Technical University of Denmark Building 427 Produktionstorvet 5 DK-2800 Kgs. Lyngby Denmark Institute for Automation and Applied Informatics Karlsruhe Institute of Technology 76344 Eggenstein-Leopoldshafen Germany Faculty of Engineering Port Said University Port Fuad 42526 Egypt Karlsruhe Nano Micro Facility Hermann-von-Helmholtz-Platz 1 76344 Eggenstein-Leopoldshafen Germany

This paper presents a concept for an integrated process chain for tooling production based on metal additive manufacturing. The proposed approach aims at taking advantage of a fully digitized production line, describing the main steps for the synergetic integration of the manufacturing assets. The production line entails digital infrastructure that collects and elaborate data from various monitoring sensors to execute corrective actions and continuously optimize the process. This line will bring breakthrough benefits, like flexibility and full traceability. Also challenges, as change management in the industry, skills gap, the requirement of new business models and product re-design have been addressed.

关键词： Smart manufacturing Industry 4.0 Additive Manufacturing automation Tooling

Feature Space Exploration for Motion Classification Based on Multi-Modal Sensor Data for Lower Limb Exoskeletons

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Feature Space Exploration for Motion Classification Based on...

IEEE-RAS International Conference on Humanoid Robots

作者： Tilman Daab Isabel Patzer Ralf Mikut Tamim Asfour Heinrich-Böll-Stiftung Institute for Anthropomatics and Robotics Institute for Automation and Applied Informatics (IAI) Karlsruhe Institute of Technology Germany

ISBN: (数字)9781538676301

ISBN: (纸本)9781538676318

In this paper, we address the problem of finding a minimal multi-modal sensor setup for motion classification in lower limb exoskeleton applications while maintaining the classification performance. We present an approach for a systematic exploration of the feature space and feature space dimensionality reduction for motion recognition using Hidden Markov Models (HMMs). We evaluated our approach using IMU and force sensor data with 10 subjects performing 14 different daily activities. We perform a dimensionality reduction on sensor feature level with single- and multi-subjects and we explore the feature space using fine-grained features such as the force value of a single direction. Additionally, we investigate the influence of physical characteristics on the classification quality. Our results show that a subject specific and general reduction of the sensors is possible while still achieving the same classification performance.

关键词： Exoskeletons Robot sensing systems Hidden Markov models Force sensors Dimensionality reduction Legged locomotion Force