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31st Joint Seminar on the Development of Materials Science in Research and Education, DMSRE 2022

作者： Rolník, L. Bucha, P. Lovíšková, J. Naď, M. Slovak University of Technology in Bratislava Faculty of Materials Science and Technology in Trnava Institute of Applied Informatics Automation and Mechatronics Jána Bottu 25 Trnava917 24 Slovakia Slovak Academy of Sciences Institute of Informatics Dúbravská cesta 9 Bratislava845 07 45 Slovakia

The beam structures are considered as fundamental structural elements that are widely used in mechanical and building structures. One of the important tasks in the structural design of beam structures should be the ability to prevent or reduce the level of unwanted vibrations. It is obvious that the dynamic properties of beam structures depend on their shape structure, i.e. from their geometric parameters and material properties. Usually, the beam structures have constant cross-section and made of homogeneous material. However, these structures in many cases do not have the required dynamic properties. The design and analysis of the beam structures that will allow its spatial properties (mass and stiffness) to be redistributed using the sliding core inserted into the beam structure is investigated in this paper. © Published under licence by IOP Publishing Ltd.

关键词： Structural properties

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Implementation of Digital Shadow Concept in the Field of Industrial automation

Implementation of Digital Shadow Concept in the Field of Ind...

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Computer Science (COMSCI), International Scientific Conference on

作者： Radovan Peter Martin Juhás Bohuslava Juhásová Institute of Applied Informatics Automation and Mechatronics Faculty of Materials Science and Technology in Trnava Slovak University of Technology in Bratislava Trnava Slovakia

We are currently witnessing a gradual increase in interest in digital shadow and digital twin solutions in the field of industrial automation. These solutions require advanced approaches in the process of their creation and are gradually becoming an integral part of industrial automation. This paper focuses on the creation of a digital shadow of a physical robotic cell and progressively goes through all the phases from the analysis of communication problems through the design of possible solutions to the actual implementation of one of the proposed solutions. The connection between the virtual robotic cell and the industrial camera is based on the processing of the data sent by the camera located above the physical robotic cell. This data is processed by a program in the PLC and then transferred to the virtual environment of the digital shadow by communication between the two OPC servers. This paper provides a more detailed overview of one possible technique for connecting the virtual environment with the physical robotic cell.

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Grid supporting nonlinear control for AC-coupled DC Microgrids

Grid supporting nonlinear control for AC-coupled DC Microgri...

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IEEE International Conference on DC Microgrids (ICDCM)

作者： Ömer Ekin Filipe Perez Friedrich Wiegel Veit Hagenmeyer Gilney Damm Institute for Automation and Applied Informatics Karlsruhe Institute of Technology (KIT) Karlsruhe Germany Supergrid Institute Lyon France COSYS-IMSE Laboratory University Gustave Eiffel Marne-la-Vallée France

ISBN: (数字)9798350363630

ISBN: (纸本)9798350363647

Direct Current (DC) Microgrids have risen as a good option for better integrating distributed renewable energies and various highly dynamic DC loads, such as electrical vehicle supply equipment or industrial grids. However, for the large-scale deployment of such DC Microgrids, developing enhanced control strategies for these inverter-based, highly dynamic grids is crucial. The present paper introduces an innovative nonlinear control tailored for DC Microgrids supporting Alternative Current (AC) grids. Building upon existing methods, we extend the nonlinear control paradigm by incorporating multiple batteries and an AC link. This integration not only bolsters support for the AC grid, but also enhances the stability of DC voltage. We validate the effectiveness of the proposed algorithm through experimental evaluations conducted on a Power Hardware-in-the-Loop (PHIL) DC Microgrid configuration and compare it to a cascaded PI controller based on a linearized model.

关键词： Power system dynamics Microgrids Power system stability Stability analysis Batteries Maintenance Voltage control

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Security Fence Inspection at Airports Using Object Detection

arXiv

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arXiv 2023年

作者： Friederich, Nils Specker, Andreas Beyerer, Jürgen Karlsruhe Institute of Technology Institute for Automation and Applied Informatics Germany Karlsruhe Institute of Technology Institute for Anthropomatics and Robotics Germany Fraunhofer IOSB Fraunhofer Center for Machine Learning

To ensure the security of airports, it is essential to protect the airside from unauthorized access. For this purpose, security fences are commonly used, but they require regular inspection to detect damages. However, due to the growing shortage of human specialists and the large manual effort, there is the need for automated methods. The aim is to automatically inspect the fence for damage with the help of an autonomous robot. In this work, we explore object detection methods to address the fence inspection task and localize various types of damages. In addition to evaluating four State-of-the-Art (SOTA) object detection models, we analyze the impact of several design criteria, aiming at adapting to the task-specific challenges. This includes contrast adjustment, optimization of hyperparameters, and utilization of modern backbones. The experimental results indicate that our optimized You Only Look Once v5 (YOLOv5) model achieves the highest accuracy of the four methods with an increase of 6.9% points in Average Precision (AP) compared to the baseline. Moreover, we show the real-time capability of the model. The trained models are published on GitHub: https://***/NFriederich/airport fence inspection. © 2023, CC BY.

关键词： Fences

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Direct Window-to-Wall Ratio Prediction Using Deep Learning Approaches

Direct Window-to-Wall Ratio Prediction Using Deep Learning A...

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Urban Remote Sensing Joint Event

作者： Xiangyu Zhuo Jiaojiao Tian Karl-Heinz Häfele Remote Sensing Technology Institute German Aerospace Center Weßling Germany Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Eggenstein-Leopoldshafen Germany

A building’s window-to-wall ratio (WWR) plays a critical role in estimating heat loss, solar gain and daylighting levels, and is therefore essential for building energy modeling applications. Typically, an accurate WWR estimation corresponds to an accurate window segmentation result, which requires high quality rectified and annotated façade images. In this paper, we propose a novel end-to-end regression model that directly predicts the invisible building attribute, the WWR, from façade imagery. For comparison, we have adopted the latest proposed semantic segmentation of windows from façade images and calculate the WWR based on the result of the semantic segmentation. These two approaches are performed and compared on three public façade benchmarks. The experimental results demonstrate that the direct prediction of invisible building attributes is feasible. Furthermore, the regression-based approach can achieve similar WWR accuracy as the segmentation-based method when they use the same backbone.

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EmbedTrack - Simultaneous Cell Segmentation and Tracking Through Learning Offsets and Clustering Bandwidths

arXiv

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arXiv 2022年

作者： Löffler, Katharina Mikut, Ralf The Institute for Automation and Applied Informatics The Institute of Biological and Chemical Systems Biological Information Processing Karlsruhe Institute of Technology Eggenstein-Leopoldshafen 76344 Germany The Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Eggenstein-Leopoldshafen 76344 Germany

A systematic analysis of the cell behavior requires automated approaches for cell segmentation and tracking. While deep learning has been successfully applied for the task of cell segmentation, there are few approaches for simultaneous cell segmentation and tracking using deep learning. Here, we present EmbedTrack, a single convolutional neural network for simultaneous cell segmentation and tracking which predicts easy to interpret embeddings. As embeddings, offsets of cell pixels to their cell center and bandwidths are learned. We benchmark our approach on nine 2D data sets from the Cell Tracking Challenge, where our approach performs on seven out of nine data sets within the top 3 contestants including three top 1 performances. The source code is publicly available at https://***/kit-loe-ge/embedtrack. Copyright © 2022, The Authors. All rights reserved.

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Customized Uncertainty Quantification of Parking Duration Predictions for EV Smart Charging

TechRxiv

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TechRxiv 2023年

作者： Phipps, Kaleb Schwenk, Karl Briegel, Benjamin Mikut, Ralf Hagenmeyer, Veit Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Hermann-von-Helmholtz-Platz 1 Karlsruhe Germany Mercedes-Benz AG Sindelfingen Germany

As Electric Vehicle (EV) demand increases, so does the demand for efficient Smart Charging (SC) applications. However, SC is only acceptable if the EV user’s mobility requirements and risk preferences are fulfilled, i.e. their respective EV has enough charge to make their planned journey. To fulfill these requirements and risk preferences, the SC application must consider the predicted parking duration at a given location and the uncertainty associated with this prediction. However, certain regions of uncertainty are more critical than others for user-centric SC applications, and therefore, such uncertainty must be explicitly quantified. Therefore, the present paper presents multiple approaches to customize the uncertainty quantification of parking duration predictions specifically for EV user-centric SC applications. We decompose parking duration prediction errors into a critical component which results in undercharging, and a non-critical component. Furthermore, we derive quantile-based security levels that can minimize the probability of a critical error given a user’s risk preferences. We evaluate our customized uncertainty quantification with four different probabilistic prediction models on an openly available semi-synthetic mobility data set and a data set consisting of real EV trips. We show that our customized uncertainty quantification can regulate critical errors, even in challenging real-world data with high fluctuation and uncertainty. © 2023, CC BY.

关键词： Uncertainty analysis

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Prediction of Parkinson’s disease based on artificial neural networks using speech datasets

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Journal of Ambient Intelligence and Humanized Computing 2023年第10期14卷 13571-13584页

作者： Liu, Wei Liu, Jierong Peng, Tao Wang, Guojun Balas, Valentina Emilia Geman, Oana Chiu, Hung-Wen School of Information and Communication Engineering Hainan University Hainan Haikou570228 China School of Computer Science and Cyber Engineering Guangzhou University Guangdong Guangzhou510006 China College of Biomedical Information and Engineering Hainan Medical University Street Hainan Haikou571199 China Department of Automation and Applied Informatics Aurel Vlaicu University of Arad Arad310130 Romania Department of Health and Human Development Stefan cel Mare University of Suceava Suceava720229 Romania Graduate Institute of Biomedical Informatics Taipei Medical University Taipei Taiwan

Parkinson’s disease (PD) is a progressive disorder of the nervous system that affects movement. Early prediction of PD can increase the chances of earlier intervention and delay the onset of the disease. Vocal impairment is one of the most important signs in the early stages of PD. Therefore, PD detection based on speech analysis and vocal patterns has attracted significant attention recently. In this paper, we propose a vowel-based artificial neural network (ANN) model for PD prediction based on single vowel phonation. Firstly, we propose a novel multi-layer neural network based on speech features to predict PD. The speech samples from 48 PD patients and 20 healthy individuals are processed into four types: vowel, number, word, and short sentence. Secondly, we establish ANN models with single-type speech samples versus combinations of multi-type speech samples, respectively. Comparative experiments demonstrate that the single-type vowel model is superior to other single-type models as well as multi-type models. Finally, we build a vowel-based ANN model for PD prediction and evaluate its performance. Extensive experiments demonstrate that the proposed model has a prediction accuracy of 91%, sensitivity of 99%, specificity of 82%, and area under the receiver operating characteristic curve (AUC) of 91%, which is superior to the performance of previous methods. Overall, this study demonstrates that the proposed model can provide good classification accuracy for predicting PD and can improve the rate of early diagnosis. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Network layers

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Experimental Evaluation and Systematic-Error Reduction of Frequency Estimation Using the Zero-Crossing Technique for Dynamic Power Grids

Experimental Evaluation and Systematic-Error Reduction of Fr...

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Conference on Smart Grid Synchronized Measurements and Analytics (SGSMA)

作者： Ellen Fö rstner Richard Jumar Orhan Delil Tanrikulu Heiko Maaß Uwe Kü hnapfel Veit Hagenmeyer Institute for Automation and Applied Informatics (IAI) Karlsruhe Institut of Technologie (KIT) Karlsruhe Germany

ISBN: (数字)9781665498234

ISBN: (纸本)9781665498241

Fundamental frequency estimates are essential for control and monitoring purposes in power systems. Hence, many devices exist that report frequency values. Future power system dynamics call for accurate frequency estimates with high reporting rates and a detailed determination of the measurement uncertainty. For a sophisticated analysis, even the used estimation algorithm becomes increasingly important in order to interpret the results. In this context, we present the evaluation of the well-known zero-crossing technique. We apply dedicated test signals in a hardware-based measurement setup and determine the resulting error metrics. We highlight the importance of differentiating between steady-state and dynamic test conditions, propose steps for an enhanced error assessment, and suggest possible ways to reduce systematic errors. We successfully implement an approach to reduce systematic errors under dynamic conditions and thereby significantly improve the comparability of frequency estimates of the zero-crossing algorithm.

关键词： Systematics Power measurement Heuristic algorithms Power system dynamics Measurement uncertainty Frequency estimation Steady-state

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The Impact of Local Energy Market Participants’ Decisions on Efficient Energy Usage: Design of a Multi-Agent System

The Impact of Local Energy Market Participants’ Decisions o...

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International Conference on European Electricity Market, EEM

作者： Christina Speck Saskia Bluhm Christof Weinhardt Philipp Zwickel Veit Hagenmeyer Institute of Information Systems and Marketing (IISM) Karlsruhe Institute of Technology (KIT) Karlsruhe Germany Institute for Automation and Applied Informatics (IAI) Karlsruhe Institute of Technology (KIT) Eggenstein-Leopoldshafen Germany

In recent years, many studies have been conducted on local energy markets. While local energy markets are mostly modeled with economically rational participants, many house- holds also consider energy quality, such as energy locality, in their energy choices. Therefore, we propose to include participants’ heterogeneous preferences from energy locality to economic rationality in the design of a multi-agent system to ensure a more efficient use of locally generated energy, which helps to increase the attractiveness of participating in local energy markets. Our results show that our design can fulfill the individual preferences of local energy market participants and prove that energy is used most efficiently with a dominant local preference group.

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