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27th CIRP Life Cycle engineering Conference, LCE 2020

作者： Kutscher, Vladimir Ruland, Sebastian Müller, Patrick Wasser, Nathan Lochau, Malte Anderl, Reiner Schürr, Andy Mezini, Mira Hähnle, Reiner Department of Computer Integrated Design TU Darmstadt Otto-Berndt-Str. 2 Darmstadt64287 Germany Real-Time Systems Lab TU Darmstadt Magdalenenstr. 4 Darmstadt64289 Germany Software Technology Group TU Darmstadt Hochschulstr. 10 Darmstadt64287 Germany Software Engineering Group TU Darmstadt Hochschulstr. 10 Darmstadt64287 Germany

Software has become an indispensable part of industrial production and thus influences the life cycle of manufacturing systems, as many of these systems have to be replaced or evolved due to changing requirements. Software adaptation through continuous evolution extends the service time of these systems and thus saves valuable resources. In this paper we present an interdisciplinary methodology for reengineering legacy software to increase the productive lifetime. Our proposed approach systematically reuses implicit domain knowledge to evolve, validate and commission new software from legacy code. The approach is evaluated on a CNC machine as a special type of industrial system. © 2020 The Author(s). Published by Elsevier B.V.

关键词： Reengineering

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Distributed nonlinear model predictive control and metric learning for heterogeneous vehicle platooning with cut-in/cut-out maneuvers

arXiv

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arXiv 2020年

作者： Basiri, Mohammad Hossein Ghojogh, Benyamin Azad, Nasser L. Fischmeister, Sebastian Karray, Fakhri Crowley, Mark The Department of Systems Design Engineering University of Waterloo ON Canada The Department of Electrical and Computer Engineering University of Waterloo ON Canada The Smart Hybrid and Electric Vehicles Systems Lab The Real-Time Embedded Software Lab The Machine Learning Lab The Centre for Pattern Analysis and Machine Intelligence

Vehicle platooning has been shown to be quite fruitful in the transportation industry to enhance fuel economy, road throughput, and driving comfort. Model Predictive Control (MPC) is widely used in literature for platoon control to achieve certain objectives, such as safely reducing the distance among consecutive vehicles while following the leader vehicle. In this paper, we propose a Distributed Nonlinear MPC (DNMPC), based upon an existing approach, to control a heterogeneous dynamic platoon with unidirectional topologies, handling possible cut-in/cut-out maneuvers. The introduced method addresses a collision-free driving experience while tracking the desired speed profile and maintaining a safe desired gap among the vehicles. The time of convergence in the dynamic platooning is derived based on the time of cut-in and/or cut-out maneuvers. In addition, we analyze the improvement level of driving comfort, fuel economy, and absolute and relative convergence of the method by using distributed metric learning and distributed optimization with Alternating Direction Method of Multipliers (ADMM). Simulation results on a dynamic platoon with cut-in and cut-out maneuvers and with different unidirectional topologies show the effectiveness of the introduced method. Copyright © 2020, The Authors. All rights reserved.

关键词： Topology

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Towards a Circular Economy of Industrial Software

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Procedia CIRP 2020年 90卷 37-42页

作者： Vladimir Kutscher Sebastian Ruland Patrick Müller Nathan Wasser Malte Lochau Reiner Anderl Andy Schürr Mira Mezini Reiner Hähnle Department of Computer Integrated Design TU Darmstadt Otto-Berndt-Str. 2 Darmstadt 64287 Germany Real-Time Systems Lab TU Darmstadt Magdalenenstr. 4 64289 Darmstadt Germany Software Technology Group TU Darmstadt Hochschulstr. 10 Darmstadt 64287 Germany Software Engineering Group TU Darmstadt Hochschulstr. 10 Darmstadt 64287 Germany

关键词： Software reengineering Industrial software Digital twin Industry 4.0 Legacy system computerized numerical control Circular economy

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Force Prediction in Robotized Ultrasonic Sheet Metal Forming Based on Finite Element Analysis and Artificial Neural Networks

Research Square

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Research Square 2021年

作者： Ostasevicius, Vytautas Paulauskaite-Taraseviciene, Agne Paleviciute, Ieva Jurenas, Vytautas Griskevicius, Paulius Eidukynas, Darius Kizauskiene, Laura Institute of Mechatronics Kaunas University of Technology Studentu 56-122 KaunasLT-51424 Lithuania Department of Applied Informatics Kaunas University of Technology Studentu 50-214 KaunasLT-51368 Lithuania Institute of Mechatronics Kaunas University of Technology Studentu 56-105 KaunasLT-51424 Lithuania Institute of Mechatronics Kaunas University of Technology Studentu 56-107 KaunasLT-51368 Lithuania Department of Mechanical Engineering Kaunas University of Technology Studentu 56-301 KaunasLT-51424 Lithuania Institute of Mechatronics Kaunas University of Technology Studentu 56-125 KaunasLT-51424 Lithuania Centre of Real Time Computer Systems Kaunas University of Technology Barsausko 59-51423 KaunasLT-51368 Lithuania

The forces acting in the process of single point incremental forming (SPIF) change the geometry of the sheet metal. The tool-workpiece interaction process is non-linear due to the large deformations of the sheet metal, which determine the plastic behaviour, as well as the evolutionary boundary conditions resulting from the contact between the tool and the sheet. Instead of lubricating the contact surface of the forming tool and the sheet metal, an innovative environmentally friendly method to reduce the coefficient of friction by vibrating the sheet has been proposed. The finite element method (FEM) allowed a virtual evaluation of the deformation parameters of the SPIF process in order to determine the destructive loads. The FEM was chosen as a deterministic numerical tool to evaluate the set of defect parameters induced by forming forces. The paper also proposes a method for predicting the formation force using an artificial neural network (ANN), assuming that such a model is generalized to implicit data. In this context, an empirical analysis of the implementation of the ANN technique is performed. © 2021, CC BY.

关键词： Sheet metal

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Comparison of Learning Cellular Computational Networks with EKF and CPSO for Multi-Location Wind Speed Prediction 8

Comparison of Learning Cellular Computational Networks with ...

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8th IEEE Symposium Series on Computational Intelligence, SSCI 2018

作者： Pathiravasam, Hirath Venayagamoorthy, Ganesh Real-Time Power and Intelligent Systems Laboratory Holcombe Department of Electrical and Computer Engineering Clemson University ClemsonSC29634 United States Department of Electrical Engineering University of Moratuwa Katubedda Sri Lanka School of Engineering University of KwaZulu-Natal Durban South Africa

ISBN: (纸本)9781538692769

Multiple-location wind speed prediction is a challenging and important problem in the modern power system operations. A distributed computing approach is implemented for spatio-temporal wind speed predictions. The multi-location wind speed prediction problem is carried out by respective cells in the Cellular Computational Network (CCN) framework. CCN based systems capture spatial dynamics via connections to neighboring cells. CCN is a framework for learning of learning systems. A Jordan-Elman hybrid recurrent neural network is used as the computational unit in the CCN. In this study, the effectiveness of Extended Kalman Filter (EKF) algorithm and Cooperative Particle Swarm optimization (CPSO) based learning algorithms in cell parameter estimation is compared. CCNs with neural networks (NNs) as computational units are essentially deep NNs via several neighbor cell connections. Three spatially distributed wind power stations are modeled using CCN framework to predict the respective wind speeds. Typical results illustrates that skill factors with EKF learning are positive and those with CPSO learning are negative. © 2018 IEEE.

关键词： Learning systems

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Digital Contact Tracing Service: An improved decentralized design for privacy and effectiveness

arXiv

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arXiv 2020年

作者： Holzapfel, Kilian Karl, Martina Lotz, Linus Carle, Georg Djeffal, Christian Fruck, Christian Haack, Christian Heckmann, Dirk Kindt, Philipp H. Köppl, Michael Krause, Patrick Shtembari, Lolian Marx, Lorenz Meighen-Berger, Stephan Neumair, Birgit Neumair, Matthias Pollmann, Julia Pollmann, Tina Resconi, Elisa Schönert, Stefan Turcati, Andrea Wiesinger, Christoph Zattera, Giovanni Allan, Christopher Barco, Esteban Bitterschulte, Kai Buchwald, Jörn Fischer, Clara Gampe, Judith Häcker, Martin Islami, Jasin Pomplun, Anatol Preisner, Sebastian Quast, Nele Romberg, Christian Steinlehner, Christoph Ziehm, Tjark Department of Physics Technical University of Munich Garching bei München Germany Max Planck Institute for Physics Munich Germany Department of Informatics Technical University of Munich Garching bei München Germany TUM School of Governance Munich Center for Technology in Society Technical University of Munich Munich Germany TUM School of Governance Department of Informatics Chair of Law and Security in Digital Transformation Technical University of Munich Munich Germany Department of Electrical and Computer Engineering Chair of Real-Time Computer Systems Technical University of Munich Munich Germany ito-app Ober-Olm Germany

We propose a decentralized digital contact tracing service that preserves the users’ privacy by design while complying to the highest security standards. Our approach is based on Bluetooth and measures actual encounters of people, the contact time period, and estimates the proximity of the contact. We trace the users’ contacts and the possible spread of infectious diseases while preventing location tracking of users, protecting their data and identity. We verify and improve the impact of tracking based on epidemiological models. We compare a centralized and decentralized approach on a legal perspective and find a decentralized approach preferable considering proportionality and data minimization. Copyright © 2020, The Authors. All rights reserved.

关键词： Bluetooth

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Scene Classification via Learning a Multi-Branch Convolutional Network

Scene Classification via Learning a Multi-Branch Convolution...

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IEEE International Conference on systems, Man and Cybernetics

作者： Xiaoyong Bian Chunfang Chen Yang Chen Xiongjun Fei Jingshan Tang Intelligent Information Processing and Real-time Industrial Systems Hubei Province Key Laboratory School of Computer Science and Technology Wuhan University of Science and Technology and Institute of Big Data Science and Engineering Wuhan University of Science and Technology Wuhan China School of Information Science and Engineering and Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education Wuhan University of Science and Technology Wuhan China School of Technology Michigan Technological University Michigan USA

Efficient and accurate classification of high-resolution scene remains a challenge of within-class diversity and between-class similarity due to rich image variations in viewpoint, object pose, spatial resolution and background. To address these issues, we propose a multi-branch convolutional neural network (MB-CNN), which focuses on tackling the problem of learning the appropriate representation of a high-resolution scene that is rich enough to discriminate between different semantic classes. First, pyramid scene parsing network (PSPNet) with minor modification is introduced to gather global object information. Then, an attention net is proposed to highlight transformation invariance and key regions for attention feature extraction. Finally, above two branches are fused with original input branch to learn consistently semantic class information and generate powerful predictions. Our approach achieves better performance favorably against state-of-the-arts on two publicly available scene datasets.

关键词： Conferences Cybernetics

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Distributed Dynamic State Estimation for Smart Grid Transmission System

引用

IFAC-PapersOnLine 2017年第2期50卷 98-103页

作者： Rahman, Md Ashfaqur Venayagamoorthy, Ganesh Kumar Real-Time Power and Intelligent Systems Laboratory ClemsonSC29634 United States Dept. of Electrical and Computer Engineering North South University Dhaka Bangladesh School of Engineering University of Kwazulu-Natal Durban4041 South Africa

The advancement of synchrophasor technologies validates the dynamic nature of the state variables of a power system. On the other hand, the need for privacy of data in the deregulated market and speed of computation for large power systems present the importance of a fast distributed state estimation method. To serve both purposes, a distributed dynamic state estimator is developed in this study. The advantages of a predictor, the constant Jacobian method and a combining unit are utilized. The system is distributed to the bus level that makes the distributed approach a parallel estimation. The accuracy and the smoothness of the proposed method are shown to be satisfactory through simulation on the IEEE 68-bus test power system. It is also shown that the proposed method can execute faster than the fastest data rate of the phasor measurement units currently deployed in the power system. © 2017

关键词： Distributed computer systems Data privacy Deregulation Dynamics Electric power transmission Electric power transmission networks Estimation Phasor measurement units Smart power grids State estimation Computational networks distributed Distributed approaches Distributed dynamics Distributed state estimation Large power systems Parallel estimation State prediction

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Cellular Computational Networks based Voltage Contingency Ranking Regarding Power System Security

Cellular Computational Networks based Voltage Contingency Ra...

引用

Clemson University Power systems Conference (PSC)

作者： Lili Wu Ganesh K. Venayagamoorthy Ronald G. Harley Jinfeng Gao Real-Time Power and Intelligent Systems Laboratory Clemson University Clemson SC 29634 USA School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta USA School of Electrical Engineering Zhengzhou University Zhengzhou China

ISBN: (纸本)9781728103174;9781728103167

Contingency ranking in simple terms is a process deployed to select the most serious contingencies that happen to the power system in the event of unplanned component outages or topological changes. In this study, Cellular Jordan Recurrent Networks (CJRNs) based non-linear voltage prediction is used for contingency ranking. The CJRNs based predicted voltages are used in computing performance indices that give proximity to voltage collapse severity of contingencies. The problem is solved combining with concentric relaxation method to reduce computation time. In order to show the advantages of the proposed CJRNs method, single line contingency ranking is tested by CJRNs voltage prediction model trained by different time steps. A novel voltage prediction correction mechanism which is applied after the prediction process shows to be effective for all time steps based contingency ranking.

关键词： Power systems Training Generators Performance analysis Voltage measurement Predictive models Databases

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Using Coalition Games for QoS Aware Scheduling in mmWave WPANs

Using Coalition Games for QoS Aware Scheduling in mmWave WPA...

引用

IEEE Vehicular Technology Conference

作者： Yali Chen Yong Niu Bo Ai Zhangdui Zhong Dapeng (Oliver) Wu Kai Li School of Electronic and Information Engineering of Beijing Jiaotong University Beijing China Department of Electrical and Computer Engineering University of Florida Gainesville FL USA Real-Time and Embedded Computing Systems Research Centre (CISTER) Porto Portugal

With the increasing quality of service (QoS) demands for indoor multimedia applications, millimeter wave (mmWave) communications are emerging as a promising candidate for the wireless personal area networks (WPANs). On the one hand, it has the advantage of providing several-Gbps transmission rate. However, due to the unique characteristics in 60-GHz frequency band, such as high propagation loss, beamforming is fully exploited for mmWave links to achieve directional transmission and reception. In this paper, we propose a novel QoS aware scheduling algorithm for concurrent transmission in mmWave WPANs based on coalition game. First, we formulate the problem of concurrent transmission scheduling into a non-convex integer programming problem. Then, we propose a coalition game based algorithm to maximize the number of flows satisfying the corresponding QoS requirements, while improving the network resource utilization effectively. Besides, our proposed algorithm converges to a Nash-stable equilibrium with greatly reduced complexity. Through extensive simulations under various system parameters, we demonstrate our scheme achieves better network performance in terms of the throughput and the number of flows scheduled successfully, compared with existed protocols.

关键词： Quality of service Interference Games Wireless personal area networks Resource management Scheduling algorithms Transmitters

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