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IEEE Green Energy and systems Conference (IGESC)

作者： Armin Buchroithner Christof Birgel Timon Bertignoll Tobias Mitter Simon Wellenzohn Alexander Schimanofsky Peter Brunnhofer Sebastian Demuth Institute of Electrical Measurement and Sensor Systems Graz University of Technology Graz Austria SSC Railtec GmbH Graz Austria Easelink GmbH Graz Austria RCC Railway Competence and Certification GmbH Graz Austria Institute of Structural Durability and Railway Technology Graz University of Technology Graz Austria

ISBN: (数字)9798350379617

ISBN: (纸本)9798350379624

The decarbonization of the transport sector, covering cargo as well as passenger mobility, plays a key role in the context of the energy revolution as it contributes to 24% of the global CO 2 emissions. Battery electric vehicles (BEVs) are part of the solution, especially if their specific drawbacks, such as limited range and availability of charging infrastructure, are mitigated by combining them with electrified rail transport. This paper describes the implementation of partially automated BEV charging capabilities into rail wagons (motorail), with an emphasis on electric power flow from the overhead catenary system to the BEVs. First, a requirement profile is determined based on different use cases, leading to several design variants relying on different power electronics modules. Subsequently, an automated underbody charging interface for the BEV is discussed in depth supported by actual test rig data.

关键词： Rails Green energy Electric vehicles Smart systems Low carbon economy Power electronics Batteries Power systems Carbon emissions

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VelObPoints: a Neural Network for Vehicle Object Detection and Velocity Estimation for Scanning LiDAR sensors

VelObPoints: a Neural Network for Vehicle Object Detection a...

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IEEE sensorS

作者： Lukas Haas Nico Leuze Arsalan Haider Matthias Kuba Thomas Zeh Alfred Schöttl Martin Jakobi Alexander W. Koch Institute for Driver Assistance Systems and Connected Mobility Kempten University of Applied Sciences Germany Institute for Measurement Systems and Sensor Technology Technical University of Munich Germany Institute for Applications of Machine Learning and Intelligent Systems University of Applied Sciences Munich Germany Faculty of Electrical Engineering University of Applied Sciences Kempten Germany

ISBN: (数字)9798350363517

ISBN: (纸本)9798350363524

LiDAR sensors are crucial sensors for highly automated vehicles. Relevant information about the vehicle's sur-roundings can be obtained from 3D point clouds, which serves as a basis for decision-making for further control of the vehicle. For this purpose, information about the type and position of objects in the vehicle's environment and their velocity and movement direction is essential. In this paper, we present VelObPoints, a neural network architecture that estimates the longitudinal and lateral velocity and performs object detection on a single LiDAR point cloud. Compared to existing tracking methods, the neural network allows this information to be extracted from a single LiDAR frame. We propose a simulated dataset for training and testing, containing motion distortion effects. The neural network achieves a mean Intersection over Union of 0.863 and a mean average velocity error of 0.332 m s −l . Based on a single point cloud, this information, consisting of the object's scaling, position, and rotation, as well as its velocities in the longitudinal and lateral directions, is immediately available for the driving function. For subsequent motion prediction, and object tracking, leading to more quickly available velocity and motion direction information and higher redundancy in sensor data fusion.

关键词： Point cloud compression Laser radar Three-dimensional displays Neural networks Data integration sensor fusion Distortion sensor systems sensors Object tracking

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Predictive modelling of cryogenic hard turning of AISI 52100 based on response surface methodology for the use in soft sensors 6th

Predictive modelling of cryogenic hard turning of AISI 52100...

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6th CIRP Conference on Surface Integrity, CSI 2022

作者： Grossmann, Felix Basten, Stephan Kirsch, Benjamin Ankener, Werner Smaga, Marek Beck, Tilmann Uebel, Julian Seewig, Jörg Aurich, Jan C. Institute For Manufacturing Technology And Production Systems Tu Kaiserslautern Gottlieb-Daimler-Str. Kaiserslautern67663 Germany Institute Of Materials Science And Engineering Tu Kaiserslautern Gottlieb-Daimler-Str. Kaiserslautern67663 Germany Institute For Measurement And Sensor Technology Tu Kaiserslautern Gottlieb-Daimler-Str. Kaiserslautern67663 Germany

This paper focuses on the development of an empirical model for the use in soft sensors to predict the surface layer properties of AISI 52100 machined via cryogenic hard turning. By using response surface methodology, a multiple regression is performed to achieve correlations between the process parameters, the process forces and the temperature, as well as the residual stresses and the austenite content. The results show a predictive R2 of over 0.75 and indicate that the empirical model can be used in soft sensors for a process control of cryogenic hard turning. © 2022 The Authors.

关键词： Cryogenics

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Remote platform for lung monitoring based on Electrical Impedance Tomography measurements 19

Remote platform for lung monitoring based on Electrical Impe...

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19th IEEE International Multi-Conference on systems, Signals and Devices, SSD 2022

作者： Hafsa, Mariem Bchir, Ons Bader, Oumaima Ben Amara, Najoua Essoukri Kanoun, Olfa Université de Sousse Ecole Nationale d' Ingénieurs de Sousse LATIS- Laboratory of Advanced Technology and Intelligent Systems Sousse4023 Tunisia Professorship of Measurement and Sensor Technology Technische Universität Chemnitz Chemnitz Germany University of Sousse National EngineeringSchool of Sousse Computer Engineering Department Sousse Tunisia

ISBN: (数字)9781665471084

ISBN: (纸本)9781665471084

Electrical Impedance Tomography (EIT) is a non-invasive, non-ionizing medical imaging method that has been on the rise in biomedical field. For lung monitoring, electrodes are attached around patient's thorax. Several boundary voltages are measured on the surface of these electrodes, which result from the injection of a small alternating current. Based on these measurements, the inner conductivity distribution of the patient's lung can be determined and used for monitoring and diagnosis. EIT can be used for extended periods of time, which makes it ideal for continuous monitoring, especially for intensive care patients. Several stationary EIT-based lung monitoring platforms have been developed for this reason. In this work, however, we propose a web and cloud-based monitoring solution that allows remote access for the doctors and medical staff. The implemented solution provides real-time, continuous monitoring as well as a visualization of extracted lung features that aid the diagnosis, © 2022 IEEE.

关键词： Electrodes

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Multicore fiber with thermally expanded cores for increased collection efficiency in endoscopic imaging

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Light(Advanced Manufacturing) 2024年第4期5卷 102-109页

作者： Kinga Zolnacz Ronja Stephan Jakob Dremel Katharina Hausmann Matthias Lieβmann Michael Steinke Juergen Czarske Robert Kuschmierz Chair of Measurement and Sensor System Technique TU Dresden01069 DresdenGermany Department of Optics and Photonics Wroclaw University of Science and Technology50370 WroclawPoland Institute of Quantum Optics Leibniz University Hannover30167 HannoverGermany Competence Center for Biomedical Computational Laser Systems(BIOLAS) TU Dresden01069 DresdenGermany Else Kroner Fresenius Center for Digital Health(EKFZ) TU Dresden01307 DresdenGermany Cluster of Excellence PhoenixD Leibniz University Hannover30167 HannoverGermany

Fiber-based endoscopes are promising for minimally invasive in vivo biomedical *** fibers offer high resolution ***,to avoid image deterioration induced by inter-core coupling,significant spacing between cores is required,which limits the active image guiding area of the ***,they suffer from low light collection efficiency and decreased signal-to-noise *** this paper,we present a method to increase the collection efficiency by thermally expanding the cores at the facet of a multicore *** expansion is based on the diffusion of doping material of the cores,thus the fiber’s original outer diameter is *** enlarging the core diameter by a factor of 2.8,we increase the intensity of the transmitted light by a factor of up to *** results in a signal-to-noise ratio increase by a factor of up to 4.6 and significant improvement in the image *** improvement increases with increasing working distance but is already prominent for as small working distance as 0.5 *** feasibility of the method is proved experimentally by lensless single-shot imaging of a test chart and incoherent light reflected from clusters of *** demonstrated approach is an important tool especially in imaging of biological specimens,for which phototoxicity must be avoided,and therefore,high collection efficiency is required.

关键词： Thermally expanded cores fiber Multicore fiber Lensless imaging

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Depth-resolved characterization of cryogenic hard turned surface layer of AISI 52100 by X-ray diffraction and scanning electron microscopy investigations 6th

Depth-resolved characterization of cryogenic hard turned sur...

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6th CIRP Conference on Surface Integrity, CSI 2022

作者： Ankener, Werner Smaga, Marek Uebel, Julian Seewig, Jörg Grossmann, Felix Basten, Stephan Kirsch, Benjamin Aurich, Jan C. Beck, Tilmann Institute Of Materials Science And Engineering Tu Kaiserslautern Gottlieb-Daimler-Str. Kaiserslautern67663 Germany Institute For Measurement And Sensor Technology Tu Kaiserslautern Gottlieb-Daimler-Str. Kaiserslautern67663 Germany Institute For Manufacturing Technology And Production Systems Tu Kaiserslautern Gottlieb-Daimler-Str. Kaiserslautern67663 Germany

In this study, the cryogenic hard turned surface layer of AISI 52100 steel was characterized by depth-resolved investigations of the residual stresses and the austenitic phase content from the surface to a depth of 100 μm. The specimens were manufactured with machining parameters that were chosen based on an empiric model of cryogenic turning. Significant differences were quantified between the specimens regarding residual stresses and the austenitic phase content down to a depth of 20 μm. Here, Grazing Incidence XRD and FIB technique were applied to analyze the mechanically and thermally induced white layer regarding their austenitic phase content and microstructure. © 2022 The Authors.

关键词： Cryogenics

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A Methodology for Verifying and Validating the Functional Performance Evaluation of High-Fidelity RADAR sensor Models at Raw Data and Detection Levels

A Methodology for Verifying and Validating the Functional Pe...

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International Conference on Vision, Image and Signal Processing (ICVISP)

作者： Arsalan Haider Lukas Haas Abduelkadir Eryildirim Thomas Zeh Shotaro Koyama Nils Henning Müllner Martin Jakobi Alexander W. Koch IFM-Institute for Driving Assistance and Connected Mobility Kempten University of Applied Sciences Germany Institute for Measurement Systems and Sensor Technology Technical University of Munich Germany Infineon Technologies Austria AG Advanced Vehicle Research Institute Kanagawa Institute of Technology Japan Institute of Transportation Systems German Aerospace Center DLR Germany

ISBN: (数字)9798331541460

ISBN: (纸本)9798331541477

This paper outlines a comprehensive methodology for verifying and validating the functional performance evaluation of high-fidelity radio detection and ranging (RADAR) sensor models at both the raw data level, specifically the range map (RM) and range-Doppler map (RDM), and the detection level. Three key performance parameters (KPPs) are defined at the raw data level: the target's received power, signal-to-noise ratio (SNR), and twoway beam pattern. Four KPPs are identified at the detection level: distance, relative radial velocity, azimuth angle, and elevation angle. This study also introduces three dynamic test scenarios directly applicable to the functional evaluation assessment of RADAR sensor models. Furthermore, a toolchain is presented to integrate real-world test scenarios into the virtual environment, allowing for frame-by-frame validation of the RADAR sensor models' functional evaluation. Quantitative analysis successfully assesses differences between simulations and real measurements, employing the mean absolute percentage error (MAPE) metric. This methodology has been successfully applied to a high-fidelity RADAR sensor model featuring a multiple input and multiple output (MIMO) 2D linear spacing virtual antenna and a complete signal processing toolchain that mimics real RADAR sensors. Results show a strong correlation between the simulation and real measurements, with MAPE values below 7 % for all defined KPPs at raw data and detection levels. Using this methodology, developers can assess whether virtual RADAR sensors are ready for environmental perception during scenario-based automated driving system (ADS) testing.

关键词： Performance evaluation Correlation Radar detection Radar Radar antennas Data models Radar signal processing Distance measurement Signal to noise ratio Testing

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Post-processing for Thorax Imaging based on Electrical Impedance Tomography using Deep Learning 19

Post-processing for Thorax Imaging based on Electrical Imped...

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19th IEEE International Multi-Conference on systems, Signals and Devices, SSD 2022

作者： Haddad, Hamdi Hafsa, Mariem Bader, Oumaima Ben Amara, Najoua Essoukri Kanoun, Olfa University of Sousse National Engineering School of Sousse Computer Engineering Department Sousse Tunisia Université de Sousse Ecole Nationale d'Ingénieurs de Sousse LATIS- Laboratory of Advanced Technology and Intelligent Systems Sousse4023 Tunisia Professorship of Measurement and Sensor Technology Technische Universität Chemnitz Chemnitz Germany

ISBN: (数字)9781665471084

ISBN: (纸本)9781665471084

Electrical Impedance Tomography (EIT) for lung monitoring is a non-ionizing medical imaging technique that is on the rise in biomedical engineering research in the recent decades. It reflects the inner conductivity distribution of biological tissue based on boundary voltages that appear on the electrodes, after injecting an alternating current of a certain amplitude into the human thorax. EIT, however, has a low spatial resolution and is susceptible to errors caused by electrode movement and poor contact quality, which can lead to the generation of blurry images. Post-processing imaging techniques can, in fact, be a beneficial tool for fine-tuning this issue. The focus of this work is to implement a U-NET multi-class semantic segmentation deep learning model to post-process reconstructed EIT thorax images to enhance its characteristics such as contrast, resolution... The model has achieved an overall accuracy of 72.43%. The resulting reconstructed images have reflected the physical changes during the breathing cycle, have deleted artifacts, and have made the shapes of the lungs and heart clearer. © 2022 IEEE.

关键词： Electrodes

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A Robust Convolutional Neural Network for Electrical Impedance Tomography Image Reconstruction

A Robust Convolutional Neural Network for Electrical Impedan...

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IEEE SSD International Multi-Conference on systems, Signals and Devices

作者： Mariem Hafsa Kailun Lin Najoua Essoukri Ben Amara Olfa Kanoun Ecole Nationale d'Ingénieurs de Sousse LATIS-Laboratory of Advanced Technology and Intelligent Systems Université de Sousse Sousse Tunisie Professorship of Measurement and Sensor Technology Technische Universität Chemnitz Chemnitz Germany Department of Mechanical Engineering Technische Universität Chemnitz Chemnitz Germany

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

Electrical Impedance Tomography (EIT) is a widely used non-invasive imaging method in a variety of application fields. Most EIT applications require an accurate and high-quality reconstructed inner conductivity distribution image. Several solutions have been developed for reliable EIT image reconstruction, but they still suffer from limitations such as low spatial resolution and inconsistency in real measurement scenarios. In this paper, we propose using physically based, extensive simulation data to train a Convolutional Neural Network (CNN) model with 5-fold cross-validation. We generate a set of 10.000 data samples, which builds a comprehensive basis for its training and ensures reliability and generalization capability. The quantitative evaluation demonstrates good reconstruction performance, achieving a Mean Squared Error (MSE) of 0.0151 and an Image Correlation Coefficient (ICC) of 0.97 in the best-performing fold for the simulated data. The method's effectiveness is particularly validated through experimental measurements, where it significantly outperforms traditional Gauss-Newton reconstruction in terms of image sharpness and boundary definition. The algorithm is robust and easily applicable for EIT imaging.

关键词： Training Image quality Electrical impedance tomography Voltage measurement Impedance measurement Imaging Robustness Convolutional neural networks Spatial resolution Image reconstruction

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Millimeter wave metamaterial for high-order orbital angular momentum generation

Millimeter wave metamaterial for high-order orbital angular ...

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International Congress on Advanced Electromagnetic Materials in Microwaves and Optics (METAMATERIALS)

作者： Alexander Schossmann Michael Töfferl Alexander Bergmann Institute of Electrical Measurement and Sensor Systems Inffeldgasse33/I Graz University of Technology Graz Austria Christian Doppler Laboratory for Structured Matter Based Sensing Inffeldgasse 33/I Graz Austria

We propose a millimeter wave metamaterial for high-order OAM generation. The concept uses a reflecting metasurface irradiated by a small antenna (e.g., on-chip antenna) that emits linearly polarized millimeter waves at an operating frequency of 61 GHz. We present the numerical proof of principle using finite element simulations. Results show that OAM generation of the order of ten is feasible by varying one geometrical parameter of the metamaterial unit cell. Subsequent work is to further optimize the OAM generation by combining variations of multiple parameters to minimize the amplitude response variation and experimentally analyze the concept.

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