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SSRN 2023年

作者： Langner, Felix Frahm, Moritz Wang, Weimin Matthes, Jörg Hagenmeyer, Veit Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Eggenstein-Leopoldshafen76344 Germany Department of Engineering Technology University of North Carolina at Charlotte CharlotteNC28223 United States

Using Model Predictive Control (MPC) is a promising method for enabling grid-interactive efficient buildings. Since MPC relies on a building model and the forecasts of external disturbances to derive optimal inputs, the uncertainties due to forecast errors and model inaccuracies can deteriorate the control performance. Most existing MPC studies for built environment are deterministic MPC (DMPC) without consideration of uncertainties even though several different methods (e.g., stochastic MPC) are available in literature to deal with them. Hence, in the present paper, a hierarchical-stochastic MPC (HSMPC) is proposed and its performance is compared with deterministic, stochastic, and hierarchical MPC for power management in a residential building with distributed energy resources. The control objective is the cost-optimal scheduling of a heat pump, a battery for energy storage, and a rooftop photovoltaic system. Measurement data is used to identify the building model. The results for one-week simulation in winter show that 1) the deterministic MPC results in an unacceptable level of temperature constraint violations in two out of five rooms;2) the hierarchical MPC can reduce the temperature constraint violations to an acceptable level at the expense of increased cost;3) the stochastic MPC achieves the same reduction in temperature constraint violations as the hierarchical MPC but at slightly lower costs;and 4) the new proposed HSMPC results in both lower temperature constraint violations and lower financial expenses than the use of stochastic or hierarchical MPC individually. © 2023, The Authors. All rights reserved.

关键词： Uncertainty analysis

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Generation of synthetic Point Clouds for MEMS LiDAR Sensor

TechRxiv

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TechRxiv 2022年

作者： Berens, Felix Elser, Stefan Reischl, Markus Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Karlsruhe76344 Germany Institute for Artificial Intelligence Ravensburg-Weingarten University of Applied Sciences Weingarten88250 Germany

Sensory data is essential for the training of methods in autonomous driving like object detection, odometry, or SLAM. MEMS LiDAR sensors can be very valuable for autonomous vehicles because they are less prone to shock and wear compared to motorized optomechanical LiDAR sensors. Recording real-world data is complicated and expensive. An alternative is simulated data, but for MEMS LiDAR sensors there is no publicly available software to simulate this type of sensor. With this paper, we introduce a method to simulate data recorded by a MEMS LiDAR sensor like the Blickfeld Cube 1 (and other MEMS LiDAR sensors as well). For this, we use the open-source autonomous driving simulation environment CARLA (our method is available online1). We compare our synthetic point cloud with a real-world point cloud and evaluate the similarity. Moreover, we demonstrate the application of our method on the problem of the optimal sensor configuration. © 2022, CC BY.

关键词： Autonomous vehicles

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Modification of the sliding joint in equipment tribotestor m`89

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Materials Science Forum 2020年 994卷 296-303页

作者： Jarmila, Oravcová Eva, Labašová Slovak University of Technology Faculty of Materials Science and Technology Institute of Applied Informatics Automation and Mechatronics Bottova 25 Slovakia

This work deals with the modification of the equipment Tribotestor. The equipment was originally designed to measure the friction coefficient of sliding bearings. The structural modification of the attachment of the sliding node has expanded its use. The equipment now also allows to measure the parameters of sliding nodes, which consist of a plane surface and a cylindrical surface. Correctness of this modification was verifieded by the experimental tests performed on several samples. © 2020 Trans Tech Publications Ltd, Switzerland.

关键词： Friction

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DiversityScanner-360°: An Automated System for Digitizing Invertebrate Bulk Samples

DiversityScanner-360°: An Automated System for Digitizing I...

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Robotics, automation and Artificial Intelligence (RAAI), IEEE International Conference on

作者： Lorenz Wührl Kirsten Rotmann Rudolf Meier Emily Hartop Nathalie Klug Christian Pylatiuk Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Karlsruhe Germany Center for Integrative Biodiversity Discovery Leibniz Institute for Evolution and Biodiversity Science Museum für Naturkunde Berlin Berlin Germany

ISBN: (数字)9798350307566

ISBN: (纸本)9798350307573

Digitization of natural history collections opens many opportunities for research. Some of the data and information stored in the physical collections are made accessible without the need to move fragile specimens. In addition, digi-tized specimens allow for automated analysis, for example, with machine learning methods. Despite the enormous advantage of digitization, most specimens in natural history collections (e.g., insects) remain undigitized. One of the reasons is that the existing techniques for digitization are very time-consuming and labor-intensive, especially for small objects such as invertebrates and in particular for small specimens less than three millimeters in size. They have to be imaged with high-resolution cameras from multiple angles to capture important morphological features. Therefore, we developed the DiversityScanner-360°, which allows for the automated imaging of invertebrates from multiple directions. These images can then be used for taxonomic work or as training data for machine learning applications. Multiple focal planes can be imaged and processed into one image to obtain in-focus images of the specimens. The number of perspectives and focal planes is set by the user and adapted to individual cases. In particular, the DiversityScanner-360° offers a new way for digitizing invertebrates preserved in ethanol, leading to new discoveries and a deeper understanding of the world's biodiversity.

关键词： automation Insects Ethanol Training data Machine learning Cameras History

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Efficient Time-Delay System Optimization with Auto-Configured Metaheuristics

Efficient Time-Delay System Optimization with Auto-Configure...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Roman Senkerik Aleksander Byrski Hubert Guzowski Peter Janku Tomas Kadavy Zuzana Kominkova Oplatkova Radek Matusu Michal Pluhacek Libor Pekar Maciej Smolka Adam Viktorin Department of Informatics and Artificial Intelligence A.I. Lab Faculty of Applied Informatics Tomas Bata University in Zlin Czech Republic Department of Automation and Control Engineering Faculty of Applied Informatics Tomas Bata University in Zlin Czech Republic Institute of Computer Science AGH University of Science and Technology Krakow Poland

This paper presents an experimental study that compares the performance of four selected metaheuristic algorithms for optimizing a time delay system model. Time delay system models are complex and challenging to optimize due to their inherent characteristics, such as non-linearity, multi-modality, and constraints. The study includes an explanation of the choice and core functionality of the selected algorithms, which are both baseline and state-of-the-art variants of self-organizing migrating algorithm (SOMA), state-of-the-art variant from the Success-History-based Adaptive Differential Evolution family of algorithms, with emphasis on diverse search (DISH algorithm), and Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithm. The hyperparameters of the metaheuristic algorithms were set using the iRace automatic algorithm configuration framework. The paper emphasizes the importance of metaheuristic algorithms in control engineering for time-delay systems to develop more effective and efficient control strategies and precise model identifications. The experimental results highlight the effectiveness of the state-of-the-art algorithms with specific adaptive mechanisms like population organization process, diverse search and adaptation mechanisms ensuring a gradual transition from exploration to exploitation. Overall, this study contributes to understanding the challenges and advantages of using metaheuristic algorithms in control engineering for time delay systems. The results provide valuable insights into the performance of modern metaheuristic algorithms and can help guide the selection of appropriate adaptive mechanisms of metaheuristics.

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EasyMLServe: Easy Deployment of REST Machine Learning Services

arXiv

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arXiv 2022年

作者： Neumann, Oliver Schilling, Marcel Reischl, Markus Mikut, Ralf Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Hermann-von-Helmholtz-Platz 1 Eggenstein-Leopoldshafen76344 Germany

Various research domains use machine learning approaches because they can solve complex tasks by learning from data. Deploying machine learning models, however, is not trivial and developers have to implement complete solutions which are often installed locally and include Graphical User Interfaces (GUIs). Distributing software to various users on-site has several problems. Therefore, we propose a concept to deploy software in the cloud. There are several frameworks available based on Representational State Transfer (REST) which can be used to implement cloud-based machine learning services. However, machine learning services for scientific users have special requirements that state-of-the-art REST frameworks do not cover completely. We contribute an EasyMLServe software framework to deploy machine learning services in the cloud using REST interfaces and generic local or web-based GUIs. Furthermore, we apply our framework on two real-world applications, i. e., energy time-series forecasting and cell instance segmentation. The EasyMLServe framework and the use cases are available on GitHub. © 2022, CC BY.

关键词： Machine learning

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Discrete control algorithms for systems with big dead time for control101 MATLAB toolbox

Discrete control algorithms for systems with big dead time f...

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Cybernetics & informatics (K&I)

作者： Ruth Bars Gyula Max J. Anthony Rossiter László Keviczky Department of Automation and Applied Informatics Budapest University of Technology and Economics Budapest Hungary School of Electrical and Electronic Engineering University of Sheffield Sheffield UK Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

ISBN: (数字)9798331541811

ISBN: (纸本)9798331541828

The first course of control is under a critical review. Both the teaching material covered and the teaching methods require new considerations. Introducing interactivity in the education process makes the learning more successful and enjoyable. MATLAB provides an effective environment for learning and applying different disciplines. Control101 is a new MATLAB toolbox under development which provides tools for interactive learning of control disciplines. This paper presents the framework for teaching discrete control algorithms applied for processes containing large dead times.

关键词： Visualization Systematics PI control Reviews Education Laboratories Process control Prediction algorithms PD control MATLAB

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Robust Control of Continuous Casting Process for Various Technological Conditions

Robust Control of Continuous Casting Process for Various Tec...

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International Carpathian Control Conference (ICCC)

作者： Sohaibullah Zarghoon Luká š Bartalský Cyril Belavý Institute of Automation Measurement and applied Informatics Faculty of Mechanical Engineering Slovak University of Technology in Bratislava Bratislava Slovakia

ISBN: (数字)9781665466363

ISBN: (纸本)9781665466370

The process of continuous steel casting, specifically the part of secondary cooling, is a typical representative of a system with distributed parameters. The paper deal with the synthesis and simulation of robust control of continuous steel casting in the secondary cooling zone. During steel production, there is a smooth change between the types of steel produced (or steel grades), to which the control system must react flexibly. Therefore, a robust approach based on simple PID controllers will be chosen for control synthesis. Based on the model obtained from the ProCast software, the uncertainty limits for the simulation model will be determined. Robust control simulations will be performed on a time-space circuit, within the simulations a possible machine failure will be tested, thus a change in cooling power in individual zones. The results will be presented as temperature fields, representing the system with distributed parameters, taking into account the time and space distribution of the controlled temerature.

关键词： Robust control Casting Uncertainty Cooling Process control Production Aerospace electronics

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On Evasion of Machine Learning-based Intrusion Detection in Smart Grids

On Evasion of Machine Learning-based Intrusion Detection in ...

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IEEE International Conference on Smart Grid Communications (SmartGridComm)

作者： Aneeqa Mumrez Gustavo Sánchez Ghada Elbez Veit Hagenmeyer Institute for Automation and Applied Informatics (IAI) KASTEL Security Research Labs (SRL) Karlsruhe Institute of Technology (KIT) Institute of Information Security and Dependability KASTEL Security Research Labs (SRL) Karlsruhe Institute of Technology (KIT)

Artificial intelligence techniques play a significant role in the cybersecurity of smart grids (SGs). Machine learning (ML) algorithms can successfully differentiate between normal behaviour and attacks, even if the anomalies are a by-product of malicious techniques never seen before. However, malicious actors successfully evade ML-based intrusion detection systems (IDSs) with attacks that mimic normal operation. In the present paper, we proactively test ML-based intrusion detection algorithms against adversarial scenarios for a better understanding of malicious capabilities. We start with the implementation of attacks (reconnaissance, data modification on the fly, and denial of service against Modbus TCP) in our testbed at KASTEL Security Lab Energy. Then, we develop a prototype ML-based IDS that monitors Modbus TCP traffic. We explicitly discuss the feasibility of evasion attacks in the problem space of Modbus TCP. Finally, we report our findings and discuss challenges from the attacker’s point of view. Additionally, considering the scarcity of available datasets for effective evaluation of IDS approaches, we release our dataset which includes Modbus TCP network traffic.

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Assessing the combination of differential flatness and deterministic automata for controllable hybrid systems

Assessing the combination of differential flatness and deter...

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IEEE Conference on Decision and Control

作者： Frederik Zahn Tobias Kleinert Veit Hagenmeyer Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Karlsruhe Germany Chair of Information and Automation Systems for Process and Material Technology RWTH Aachen University Aachen Germany

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

Hybrid automata are a powerful model class to analyze and control technical systems with nonlinear continuous dynamics and discrete-event dynamics. However, due to their complexity, controllability is generally hard to achieve in hybrid automata, although it is a desirable property of engineered systems. In the present paper, we assess a novel model class of controllable deterministic hybrid automata, which we call Flat Hybrid Automata. This model class is based on the concept of differential flatness for the nonlinear continuous dynamics and on the property of determinism and controllability for the discrete event dynamics. We review the formal definition and we give the respective conditions and a proof for its controllability. An electrical network example illustrates the methodology and shows its potential regarding modelling, control and system design.

关键词： Adaptation models Analytical models Power system dynamics Automata Switches Controllability Trajectory

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