Automated dynamic safety evaluation of generic fail-operational mechatronic systems. - In: 2021 IEEE International Conference on Prognostics and Health Management (ICPHM), (2021), insges. 8 S.
The increasing complexity of connected and distributed mechatronic systems developed for safety-critical applications, as e.g. a powertrain of automated vehicles, makes their dependability evaluation a challenging task. Moreover, precise statements about the dependability metrics are of high interest for architectural decisions in the early stages of the design process. System dynamics, possible fault combinations as well as the sequence, duration and impact of various faults and the associated system states must be considered for a realistic evaluation and quantification of the failure behavior.In order to optimize the design of generic mechatronic systems at different abstraction levels and with different component characteristics, this paper examines a method to analytically quantify the stochastic behavior of a system. The proposed approach enables to significantly increase the computational efficiency of the safety analysis of generic fail-operational mechatronic systems without loss in accuracy by automating the dynamic evaluation of convolutional integrals. The application of the proposed safety analysis is demonstrated using an exemplary system with dynamic redundancy.
https://doi.org/10.1109/ICPHM51084.2021.9486670
Towards a realistic maximum flow model in hybrid multi-channel wireless mesh networks. - In: 12th Wireless Days Conference (WD 2021), (2021), insges. 8 S.
There is a continuous interest in multi-hop multichannel Wireless Mesh Networks (WMNs) based on IEEE 802.11 for years now. Many design problems have been proposed to calculate performance metrics like the maximum flow to improve network capacity. However, these usually presume global coordination of transmissions in a slotted time model, a highly questionable presumption for IEEE 802.11. Additionally to this shortcoming, existing data rate models and link correlations are not realistic, rendering results imprecise. Furthermore, they are not incorporating other means of communication, e.g., wired connections, which could exist in real-world scenarios due to geographical proximity of selected nodes or availability of access to a wired LAN or WAN infrastructure - leading to the use case of hybrid WMNs. This paper proposes a model for maximum flow in hybrid multi-channel WMNs based on shared airtime. It is tailored to incorporate an exact representation of data rates and link correlations in IEEE 802.11 networks to produce dependable results and it furthermore supports wired connections. Packet-level simulation results verify key aspects of our model, supporting its validity with only a small relative error of less than 5% on average and no severe outliers.
https://doi.org/10.1109/WD52248.2021.9508303
Dynamic system modelling and process monitoring based on long-term dependency slow feature analysis. - In: Journal of process control, ISSN 0959-1524, Bd. 105 (2021), S. 27-47
Modern industrial processes are large-scale, highly complex systems with many units and equipment. The complex flow of mass and energy, as well as the compensation effects of closed-loop control systems, cause significant cross-correlation and autocorrelation between process variables. To operate the process systems stably and efficiently, it is crucial to uncover the inherent characteristics of both the variance structure and dynamic relationship. Compared with the original slow feature analysis (SFA) that can only model the one-step time dependence, long-term dependency slow feature analysis (LTSFA) proposed in this paper can understand the longer-term dynamics by an explicit expression of latent states of the process. An iterative algorithm is developed for the model parameter optimization and its convergency is proved. The model properties and theoretical comparison with existing dynamic models are presented. A process monitoring strategy is designed based on LTSFA. The results of two simulation case studies show that LTSFA has better system dynamics extraction capability, which reduces the violation rate of the residual for the 95% confidence interval from 40.4% to 3.2% compared to the original SFA, and can disentangle the quickly- and slowly-varying features. Several typical disturbances can be correctly identified by LTSFA. The monitoring results on the Tennessee Eastman process benchmark show the overall advantages of the proposed method both in the dynamic and nominal deviation detection and the monitoring accuracy
https://doi.org/10.1016/j.jprocont.2021.07.007
A probabilistic approach to online classification of bicycle crashes. - In: Accident analysis & prevention, ISSN 1879-2057, Bd. 160 (2021), 106311
When a bicycle crash takes place, it is paramount for an emergency center to recognize the physical state of the cyclist as early as possible. However, an injured bicyclist may be incapable of making a phone call to the emergency center. In this study, we propose an online approach to classify bicycle crashes based on signals from an onboard inertial measurement unit (IMU), which can be used as a trigger function for an automatic emergency system. For this purpose, we define several bicycle crash features according to the kinematic properties of bicycle accidents. The input signals (variables) influencing the individual crash features are determined by the ANOVA method (analysis of variance). With the determined input signals, probabilistic models for each crash feature are trained on the base of logit models and 20,000 km naturalistic driving data including 20 real crashes. In addition, further crash and corner case data has been collected for the model training. A decision tree describing all probabilistic crash features is used to classify different kinematic events and crash scenarios. A series of driving tests with a crash-dummy and a crash-car are performed to verify the proposed crash classification approach, showing a sensitivity of 96.8%, a specificity of 99.6% and an accuracy of 99.5% of the trained model.
https://doi.org/10.1016/j.aap.2021.106311
Crowd-sourced plant occurrence data provide a reliable description of macroecological gradients. - In: Ecography, ISSN 1600-0587, Bd. 44 (2021), 8, S. 1131-1142
Deep learning algorithms classify plant species with high accuracy, and smartphone applications leverage this technology to enable users to identify plant species in the field. The question we address here is whether such crowd-sourced data contain substantial macroecological information. In particular, we aim to understand if we can detect known environmental gradients shaping plant co-occurrences. In this study we analysed 1 million data points collected through the use of the mobile app Flora Incognita between 2018 and 2019 in Germany and compared them with Florkart, containing plant occurrence data collected by more than 5000 floristic experts over a 70-year period. The direct comparison of the two data sets reveals that the crowd-sourced data particularly undersample areas of low population density. However, using nonlinear dimensionality reduction we were able to uncover macroecological patterns in both data sets that correspond well to each other. Mean annual temperature, temperature seasonality and wind dynamics as well as soil water content and soil texture represent the most important gradients shaping species composition in both data collections. Our analysis describes one way of how automated species identification could soon enable near real-time monitoring of macroecological patterns and their changes, but also discusses biases that must be carefully considered before crowd-sourced biodiversity data can effectively guide conservation measures.
https://doi.org/10.1111/ecog.05492
On the role of arkypallidal and prototypical neurons for phase transitions in the external pallidum. - In: The journal of neuroscience, ISSN 1529-2401, Bd. 41 (2021), 31, S. 6673-6683
The external pallidum (globus pallidus pars externa [GPe]) plays a central role for basal ganglia functions and dynamics and, consequently, has been included in most computational studies of the basal ganglia. These studies considered the GPe as a homogeneous neural population. However, experimental studies have shown that the GPe contains at least two distinct cell types (prototypical and arkypallidal cells). In this work, we provide in silico insight into how pallidal heterogeneity modulates dynamic regimes inside the GPe and how they affect the GPe response to oscillatory input. We derive a mean-field model of the GPe system from a microscopic spiking neural network of recurrently coupled prototypical and arkypallidal neurons. Using bifurcation analysis, we examine the influence of dopamine-dependent changes of intrapallidal connectivity on the GPe dynamics. We find that increased self-inhibition of prototypical cells can induce oscillations, whereas increased inhibition of prototypical cells by arkypallidal cells leads to the emergence of a bistable regime. Furthermore, we show that oscillatory input to the GPe, arriving from striatum, leads to characteristic patterns of cross-frequency coupling observed at the GPe. Based on these findings, we propose two different hypotheses of how dopamine depletion at the GPe may lead to phase-amplitude coupling between the parkinsonian beta rhythm and a GPe-intrinsic y rhythm. Finally, we show that these findings generalize to realistic spiking neural networks of sparsely coupled Type I excitable GPe neurons. - SIGNIFICANCE STATEMENT Our work provides (1) insight into the theoretical implications of a dichotomous globus pallidus pars externa (GPe) organization, and (2) an exact mean-field model that allows for future investigations of the relationship between GPe spiking activity and local field potential fluctuations. We identify the major phase transitions that the GPe can undergo when subject to static or periodic input and link these phase transitions to the emergence of synchronized oscillations and cross-frequency coupling in the basal ganglia. Because of the close links between our model and experimental findings on the structure and dynamics of prototypical and arkypallidal cells, our results can be used to guide both experimental and computational studies on the role of the GPe for basal ganglia dynamics in health and disease.
https://doi.org/10.1523/JNEUROSCI.0094-21.2021
Automatic segmentation of skin cells in multiphoton data using multi-stage merging. - In: Scientific reports, ISSN 2045-2322, Bd. 11 (2021), 14534, S. 1-19
We propose a novel automatic segmentation algorithm that separates the components of human skin cells from the rest of the tissue in fluorescence data of three-dimensional scans using non-invasive multiphoton tomography. The algorithm encompasses a multi-stage merging on preprocessed superpixel images to ensure independence from a single empirical global threshold. This leads to a high robustness of the segmentation considering the depth-dependent data characteristics, which include variable contrasts and cell sizes. The subsequent classification of cell cytoplasm and nuclei are based on a cell model described by a set of four features. Two novel features, a relationship between outer cell and inner nucleus (OCIN) and a stability index, were derived. The OCIN feature describes the topology of the model, while the stability index indicates segment quality in the multi-stage merging process. These two new features, combined with the local gradient magnitude and compactness, are used for the model-based fuzzy evaluation of the cell segments. We exemplify our approach on an image stack with 200 × 200 × 100 [my]m^3, including the skin layers of the stratum spinosum and the stratum basale of a healthy volunteer. Our image processing pipeline contributes to the fully automated classification of human skin cells in multiphoton data and provides a basis for the detection of skin cancer using non-invasive optical biopsy.
https://doi.org/10.1038/s41598-021-93682-y
Normative data and standard operating procedures for static and dynamic retinal vessel analysis as biomarker for cardiovascular risk. - In: Scientific reports, ISSN 2045-2322, Bd. 11 (2021), 14136, S. 1-12
Retinal vessel phenotype is predictive for cardiovascular outcome. This cross-sectional population-based study aimed to quantify normative data and standard operating procedures for static and dynamic retinal vessel analysis. We analysed central retinal arteriolar (CRAE) and venular (CRVE) diameter equivalents, as well as retinal endothelial function, measured by flicker light‐induced maximal arteriolar (aFID) and venular (vFID) dilatation. Measurements were performed in 277 healthy individuals aged 20 to 82 years of the COmPLETE study. The mean range from the youngest compared to the oldest decade was 196 ± 13 to 166 ± 17 µm for CRAE, 220 ± 15 to 199 ± 16 µm for CRVE, 3.74 ± 2.17 to 3.79 ± 2.43% for aFID and 4.64 ± 1.85 to 3.86 ± 1.56% for vFID. Lower CRAE [estimate (95% CI): - 0.52 (- 0.61 to - 0.43)], CRVE [- 0.33 (- 0.43 to - 0.24)] and vFID [- 0.01 (- 0.26 to - 0.00)], but not aFID, were significantly associated with older age. Interestingly, higher blood pressure was associated with narrower CRAE [- 0.82 (- 1.00 to - 0.63)] but higher aFID [0.05 (0.03 to 0.07)]. Likewise, narrower CRAE were associated with a higher predicted aFID [- 0.02 (- 0.37 to - 0.01)]. We recommend use of defined standardized operating procedures and cardiovascular risk stratification based on normative data to allow for clinical implementation of retinal vessel analysis in a personalized medicine approach.
https://doi.org/10.1038/s41598-021-93617-7
Using the multi-linear rank-(Lr, Lr, 1) decomposition for the detection of the 200 Hz band activity in somatosensory evoked magnetic fields and somatosensory evoked electrical potentials. - In: IEEE access, ISSN 2169-3536, Bd. 9 (2021), S. 106232-106244
Im Titel ist "r" tiefgestellt
https://doi.org/10.1109/ACCESS.2021.3100759
Methoden der Statistik und Prozessanalyse : eine anwendungsorientierte Einführung. - Berlin : Springer Vieweg, 2021. - 1 Online-Ressource (XXVIII, 481 Seiten). - (Springer eBook Collection) ISBN 978-3-662-61626-0
Einführung in Statistik und Datenvisualisierung -- Theoretische Grundlagen für die statistische Analyse -- Regression -- Versuchsplanung -- System Identification -- Data Mining -- Appendices: A Brief Review of Set Theory and Notation; A Traditional Approach to Ordinary, Linear Least Squares Regression’ A Traditional Approach to Weighted, Linear Least Squares Regression; A Traditional Approach to Factorial Design Analysis; Using Excel for Statistical Analysis; Using MATLAB® for Statistical Analysis.
https://doi.org/10.1007/978-3-662-61626-0