A new measurement method of contact conditions in a vacuum circuit breaker with the field emission current during the closing operation. - In: IEEE transactions on instrumentation and measurement, ISSN 0018-9456, Bd. 71 (2022), 6005111, insges. 11 S.
The performance of vacuum circuit breakers (VCBs) in the interruption and protection of an electric power system is highly dependent on the contact conditions; thus, the assessment of the contact conditions is essential. The field emission current has been recognized as an effective parameter with which to investigate the contact conditions both qualitatively and quantitatively. The traditional measurement method that uses the field emission current to determine the contact conditions is performed under ac voltage and requires the VCB to be dismantled, which limits the application of this method. The objective of this study is to develop a new measurement method for the contact conditions in a VCB with the field emission current during the closing operation under dc voltage. The new measurement method does not require the VCB to be dismantled and can simplify the processing of experimental data. Then, the new measurement method is applied to investigate the contact conditions during capacitive switching. The obtained result shows that the field enhancement factor $\beta $ of the contact surface oscillates up and down during the capacitive experiment, which can verify the conditioning and deconditioning effects of the inrush current on contact surfaces.
https://doi.org/10.1109/TIM.2022.3193710
Set-point tracking for nonlinear systems subject to uncertainties using model-following control with a high-gain controller. - In: 2022 European Control Conference (ECC), (2022), S. 1617-1622
We study the robust control problem for nonlinear systems using a model-following control (MFC) scheme. The MFC architecture is a two degrees-of-freedom structure consisting of two control loops: the model control loop (MCL) including a nominal model of the process, and the process control loop (PCL) accounting for modelling errors and disturbances. Both control loops are designed using (partial) feedback linearisation. We design a set-point controller in the MCL and apply a high-gain state feedback in the PCL. We analyse the stability of the overall system and derive robustness bounds for a class of locally Lipschitz uncertainties. We analyse and compare robustness and performance properties to various different control designs. It turns out that the proposed controller is able to stabilise significantly larger uncertainties, while requiring less control effort and shows better tracking performance.
https://doi.org/10.23919/ECC55457.2022.9838162
Suppression of natural lens fluorescence in fundus autofluorescence measurements: review of hardware solutions. - In: Biomedical optics express, ISSN 2156-7085, Bd. 13 (2022), 10, S. 5151-5170
Fluorescence lifetime imaging ophthalmoscopy (FLIO), a technique for investigating metabolic changes in the eye ground, can reveal the first signs of diseases related to metabolism. The fluorescence of the natural lens overlies the fundus fluorescence. Although the influence of natural lens fluorescence can be somewhat decreased with mathematical models, excluding this influence during the measurement by using hardware enables more exact estimation of the fundus fluorescence. Here, we analyze four 1-photon excitation hardware solutions to suppress the influence of natural lens fluorescence: aperture stop separation, confocal scanning laser ophthalmoscopy, combined confocal scanning laser ophthalmoscopy and aperture stop separation, and dual point confocal scanning laser ophthalmoscopy. The effect of each principle is demonstrated in examples. The best suppression is provided by the dual point principle, realized with a confocal scanning laser ophthalmoscope. In this case, in addition to the fluorescence of the whole eye, the fluorescence of the anterior part of the eye is detected from a non-excited spot of the fundus. The intensity and time-resolved fluorescence spectral data of the fundus are derived through the subtraction of the simultaneously measured fluorescence of the excited and non-excited spots. Advantages of future 2-photon fluorescence excitation are also discussed. This study provides the first quantitative evaluation of hardware principles to suppress the fluorescence of the natural lens during measurements of fundus autofluorescence.
https://doi.org/10.1364/BOE.462559
SVDistNet: self-supervised near-field distance estimation on surround view fisheye cameras. - In: IEEE transactions on intelligent transportation systems, Bd. 23 (2022), 8, S. 10252-10261
A 360˚ perception of scene geometry is essential for automated driving, notably for parking and urban driving scenarios. Typically, it is achieved using surround-view fisheye cameras, focusing on the near-field area around the vehicle. The majority of current depth estimation approaches focus on employing just a single camera, which cannot be straightforwardly generalized to multiple cameras. The depth estimation model must be tested on a variety of cameras equipped to millions of cars with varying camera geometries. Even within a single car, intrinsics vary due to manufacturing tolerances. Deep learning models are sensitive to these changes, and it is practically infeasible to train and test on each camera variant. As a result, we present novel camera-geometry adaptive multi-scale convolutions which utilize the camera parameters as a conditional input, enabling the model to generalize to previously unseen fisheye cameras. Additionally, we improve the distance estimation by pairwise and patchwise vector-based self-attention encoder networks. We evaluate our approach on the Fisheye WoodScape surround-view dataset, significantly improving over previous approaches. We also show a generalization of our approach across different camera viewing angles and perform extensive experiments to support our contributions. To enable comparison with other approaches, we evaluate the front camera data on the KITTI dataset (pinhole camera images) and achieve state-of-the-art performance among self-supervised monocular methods. An overview video with qualitative results is provided at https://youtu.be/bmX0UcU9wtA. Baseline code and dataset will be made public.
https://doi.org/10.1109/TITS.2021.3088950
Homogeneous Lp stability for homogeneous systems. - In: IEEE access, ISSN 2169-3536, Bd. 10 (2022), S. 81654-81683
The motivation of this paper comes from the fact that Lp−stability and Lp−gain, using the classical signal norms, is not well-defined for arbitrary continuous weighted homogeneous systems. However, using homogeneous signal norms it is possible to show that every internally stable homogeneous system has a globally defined finite homogeneous Lp−gain, for p sufficiently large. If the system has a homogeneous approximation, the homogeneous Lp−gain is inherited locally. Homogeneous Lp−stability can be characterized by a homogeneous dissipation inequality, which in the input affine case can be transformed to a homogeneous Hamilton-Jacobi inequality. An estimation of an upper bound for the homogeneous Lp−gain can be derived from these inequalities. Homogeneous L∞−stability is also considered and its strong relationship to Input-to-State stability is studied. These results are extensions to arbitrary homogeneous systems of the well-known situation for linear time-invariant systems, where the Hamilton-Jacobi inequality reduces to an algebraic Riccati inequality. A natural application of finite-gain homogeneous Lp−stability is in the study of stability for interconnected systems. An extension of the small-gain theorem for negative feedback systems and results for systems in cascade are derived for different homogeneous norms. Previous results in the literature use classical signal norms, hence, they can only be applied to a restricted class of homogeneous systems. The results are illustrated by several examples.
https://doi.org/10.1109/ACCESS.2022.3195505
Further dimensions for sensing in biofluids: distinguishing bioorganic analytes by the salt-induced adaptation of a cucurbit[7]uril-based chemosensor. - In: Journal of the American Chemical Society, ISSN 1520-5126, Bd. 144 (2022), 29, S. 13084-13095
Insufficient binding selectivity of chemosensors often renders biorelevant metabolites indistinguishable by the widely used indicator displacement assay. Array-based chemosensing methods are a common workaround but require additional effort for synthesizing a chemosensor library and setting up a sensing array. Moreover, it can be very challenging to tune the inherent binding preference of macrocyclic systems such as cucurbit[n]urils (CBn) by synthetic means. Using a novel cucurbit[7]uril-dye conjugate that undergoes salt-induced adaptation, we now succeeded in distinguishing 14 bioorganic analytes from each other through the facile stepwise addition of salts. The salt-specific concentration-resolved emission provides additional information about the system at a low synthetic effort. We present a data-driven approach to translate the human-visible curve differences into intuitive pairwise difference measures. Ion mobility experiments combined with density functional theory calculations gave further insights into the binding mechanism and uncovered an unprecedented ternary complex geometry for CB7. TThis work introduces the non-selectively binding, salt-adaptive cucurbit[n]uril system for sensing applications in biofluids such as urine, saliva, and blood serum.
https://doi.org/10.1021/jacs.2c01520
LiMoSeg: real-time Bird's Eye View based LiDAR motion segmentation. - In: , (2022), S. 828-835
LiDAR = Light Detection and Ranging
Moving object detection and segmentation is an essential task in the Autonomous Driving pipeline. Detecting and isolating static and moving components of a vehicle’s surroundings are particularly crucial in path planning and localization tasks. This paper proposes a novel real-time architecture for motion segmentation of Light Detection and Ranging (LiDAR) data. We use two successive scans of LiDAR data in 2D Bird’s Eye View (BEV) representation to perform pixel-wise classification as static or moving. Furthermore, we propose a novel data augmentation technique to reduce the significant class imbalance between static and moving objects. We achieve this by artificially synthesizing moving objects by cutting and pasting static vehicles. We demonstrate a low latency of 8 ms on a commonly used automotive embedded platform, namely Nvidia Jetson Xavier. To the best of our knowledge, this is the first work directly performing motion segmentation in LiDAR BEV space. We provide quantitative r esults on the challenging SemanticKITTI dataset, and qualitative results are provided in https://youtu.be/2aJ-cL8b0LI.
https://doi.org/10.5220/0010866000003124
Statistics for chemical and process engineers : a modern approach
Second edition. - Cham : Springer, 2022. - 1 Online-Ressource (xxx, 432 Seiten) ISBN 978-3-030-83190-5
https://ebookcentral.proquest.com/lib/kxp/detail.action?docID=6841085
Adaptive update handling for graph HTAP. - In: 2022 IEEE 38th International Conference on Data Engineering Workshops, (2022), S. 16-23
Processing hybrid transactional/analytical workloads on graph data can significantly benefit from GPU accelerators. However, to exploit the full potential of GPU processing, dedicated graph representations are necessary, which make inplace updates difficult. In this paper, we discuss an approach for adaptive handling of updates in a graph database system for HTAP workloads. We discuss and evaluate strategies for propagating updates from an update-friendly table storage to a GPU-optimized sparse matrix format.
https://doi.org/10.1109/ICDEW55742.2022.00007
Efficient and robust semantic mapping for indoor environments. - In: 2022 IEEE International Conference on Robotics and Automation (ICRA), (2022), S. 9221-9227
A key proficiency an autonomous mobile robot must have to perform high-level tasks is a strong understanding of its environment. This involves information about what types of objects are present, where they are, what their spatial extend is, and how they can be reached, i.e., information about free space is also crucial. Semantic maps are a powerful instrument providing such information. However, applying semantic segmentation and building 3D maps with high spatial resolution is challenging given limited resources on mobile robots. In this paper, we incorporate semantic information into efficient occupancy normal distribution transform (NDT) maps to enable real-time semantic mapping on mobile robots. On the publicly available dataset Hypersim, we show that, due to their sub-voxel accuracy, semantic NDT maps are superior to other approaches. We compare them to the recent state-of-the-art approach based on voxels and semantic Bayesian spatial kernel inference (S-BKI) and to an optimized version of it derived in this paper. The proposed semantic NDT maps can represent semantics to the same level of detail, while mapping is 2.7 to 17.5 times faster. For the same grid resolution, they perform significantly better, while mapping is up to more than 5 times faster. Finally, we prove the real-world applicability of semantic NDT maps with qualitative results in a domestic application.
https://doi.org/10.1109/ICRA46639.2022.9812205