Publikationen an der Fakultät für Informatik und Automatisierung ab 2015

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Schuler, Ramona; Langer, Andreas; Marquardt, Christoph; Kalev, Georgi; Meisinger, Maximilian; Bandura, Julia; Schiedeck, Thomas; Goos, Matthias; Vette, Albert; Konschake, Marko
Automatic muscle impedance and nerve analyzer (AMINA) as a novel approach for classifying bioimpedance signals in intraoperative pelvic neuromonitoring. - In: Scientific reports, ISSN 2045-2322, Bd. 14 (2024), 654, S. 1-15

Frequent complications arising from low anterior resections include urinary and fecal incontinence, as well as sexual disorders, which are commonly associated with damage to the pelvic autonomic nerves during surgery. To assist the surgeon in preserving pelvic autonomic nerves, a novel approach for intraoperative pelvic neuromonitoring was investigated that is based on impedance measurements of the innervated organs. The objective of this work was to develop an algorithm called AMINA to classify the bioimpedance signals, with the goal of facilitating signal interpretation for the surgeon. Thirty patients included in a clinical investigation underwent nerve-preserving robotic rectal surgery using intraoperative pelvic neuromonitoring. Contraction of the urinary bladder and/or rectum, triggered by direct stimulation of the innervating nerves, resulted in a change in tissue impedance signal, allowing the nerves to be identified and preserved. Impedance signal characteristics in the time domain and the time-frequency domain were calculated and classified to develop the AMINA. Stimulation-induced positive impedance changes were statistically significantly different from negative stimulation responses by the percent amplitude of impedance change Amax in the time domain. Positive impedance changes and artifacts were distinguished by classifying wavelet scales resulting from peak detection in the continuous wavelet transform scalogram, which allowed implementation of a decision tree underlying the AMINA. The sensitivity of the software-based signal evaluation by the AMINA was 96.3%, whereas its specificity was 91.2%. This approach streamlines and automates the interpretation of impedance signals during intraoperative pelvic neuromonitoring.



https://doi.org/10.1038/s41598-023-50504-7
Walther, Dominik; Junger, Christina; Schmidt, Leander; Schricker, Klaus; Notni, Gunther; Bergmann, Jean Pierre; Mäder, Patrick
Recurrent autoencoder for weld discontinuity prediction. - In: Journal of advanced joining processes, ISSN 2666-3309, Bd. 9 (2024), 100203, S. 1-12

Laser beam butt welding is often the technique of choice for a wide range of industrial tasks. To achieve high quality welds, manufacturers often rely on heavy and expensive clamping systems to limit the sheet movement during the welding process, which can affect quality. Jiggless welding offers a cost-effective and highly flexible alternative to common clamping systems. In laser butt welding, the process-induced joint gap has to be monitored in order to counteract the effect by means of an active position control of the sheet metal. Various studies have shown that sheet metal displacement can be detected using inductive probes, allowing the prediction of weld quality by ML-based data analysis. The probes are dependent on the sheet metal geometry and are limited in their applicability to complex geometric structures. Camera systems such as long-wave infrared (LWIR) cameras can instead be mounted directly behind the laser to overcome a geometry dependent limitation of the jiggles system. In this study we will propose a deep learning approach that utilizes LWIR camera recordings to predict the remaining welding process to enable an early detection of weld interruptions. Our approach reaches 93.33% accuracy for time-wise prediction of the point of failure during the weld.



https://doi.org/10.1016/j.jajp.2024.100203
Machts, René; Hunold, Alexander; Drebenstedt, Christian; Rock, Michael; Leu, Carsten; Haueisen, Jens
Rain may improve survival from direct lightning strikes to the human head. - In: Scientific reports, ISSN 2045-2322, Bd. 14 (2024), 1695, S. 1-9

There is evidence that humans can survive a direct lightning strike to the head. Our question is: could water (rain) on the skin contribute to an increase in the survival rate? We measure the influence of rain during high-energy direct lightning strikes on a realistic three-compartment human head phantom. We find a lower number of perforations and eroded areas near the lightning strike impact points on the head phantom when rain was applied compared to no rain. Current amplitudes in the brain were lower with rain compared to no rain before a fully formed flashover. We conclude that rain on the scalp potentially contributes to the survival rate of 70-90% due to: (1) lower current exposition in the brain before a fully formed flashover, and (2) reduced mechanical and thermal damage.



https://doi.org/10.1038/s41598-023-50563-w
Nycz, Julia; Link, Dietmar; Klemm, Matthias; Klee, Sascha; Haueisen, Jens
Characterization of a new fluorescence lifetime imaging ophthalmoscope. - In: Acta ophthalmologica, ISSN 1755-3768, Bd. 102 (2024), S279, insges. 1 S.

Aims/Purpose: Fluorescence lifetime imaging ophthalmoscopy (FLIO) allows in vivo measurement of autofluorescence intensity decays of endogenous fluorophores in the ocular fundus. So far, only devices from Heidelberg Engineering based on the Spectralis system have been used in FLIO research. Here, we present and characterize a new FLIO device based on the RETImap system from Roland Consult. Methods: The device is based on a confocal scanning laser ophthalmoscope (35˚ field, 512 × 512 px). A ps diode laser (BDL-SMN 473 nm, Becker & Hickl GmbH, Berlin, Germany) excites autofluorescence. The fluorescence photons are split into a short (498-560 nm, SSC) and a long (560-720 nm, LSC) spectral channel (one HPM-100-40 detector [Becker & Hickl GmbH] each) and are detected by time-correlated single photon counting (SPC-160, Becker & Hickl GmbH). We determined the maximum laser power (ILT2400, International Light Technologies, Inc. Peabody, MA, USA) and analysed the instrument's behaviour at three different laser power levels (150 μW, 200 μW and max.) in terms of laser spectrum (CAS140CT, Instrument Systems GmbH, Munich, Germany) and instrument response function (IRF). The IRF was determined using a 25 μM Eosin Y solution, mixed with a 5 M solution of potassium iodide, placed in a flat cuvette (110-OS, Hellma GmbH & Co. KG, Müllheim, Germany) in front of the objective lens of the FLIO device. Fluorescence measurements of approximately 1-min duration were performed three times for all three laser powers. The IRF and the full width at half maximum (FWHM) were calculated using FLIMX software (www.flimx.de). Results: The max. laser power was 280 μW. The peak wavelengths of the laser spectra were 467.6 (150 μW), 467.9 (200 μW) and 468.0 nm (280 μW). IRF FWHM in SSC were 298.6 ± 1.1 ps (150 μW), 341.0 ± 2.5 ps (200 μW) and 347.5 ± 6.0 ps (280 μW). In LSC, the IRF FWHM were 290.4 ± 3.8 ps (150 μW), 344.0 ± 3.4 ps (200 μW) and 358.8 ± 1.3 ps (280 μW). Results are mean ± standard deviation. Conclusions: A new fluorescence lifetime imaging ophthalmoscope has been characterized. The device offers a high laser power for fluorescence excitation, a large field of view, a high spatial resolution, and a sufficiently high time resolution. Thus, it is suitable for fluorescence lifetime studies.



https://doi.org/10.1111/aos.15921
Rezaei, Ahmad; Nau, Johannes; Streitferdt, Detlef; Schambach, Jörg; Vangelov, Todor
ReProInspect: framework for reproducible defect datasets for improved AOI of PCBAs. - In: Engineering of computer-based systems, (2024), S. 205-214

Today, the process of producing a printed circuit board assembly (PCBA) is growing rapidly, and this process requires cutting-edge debugging and testing of the boards. The Automatic Optical Inspection (AOI) process detects defects in the boards, components, or solder pads using image processing and machine learning (ML) algorithms. Although state-of-the-art approaches for identifying defects are well developed, due to three main issues, the ML algorithms and datasets are incapable of fully integrating into industrial plants. These issues are privacy limitations for sharing data, the distribution shifts in the PCBA industry, and the absence of a degree of freedom for reproducible and modifiable synthetic datasets.



https://doi.org/10.1007/978-3-031-49252-5_16
Müller, Erik; Petkoviâc, Bojana; Ziolkowski, Marek; Weise, Konstantin; Töpfer, Hannes; Haueisen, Jens
An improved GPU-optimized fictitious surface charge method for transcranial magnetic stimulation. - In: IEEE transactions on magnetics, ISSN 1941-0069, Bd. 60 (2024), 3, 5100104, insges. 4 S.

The fictitious surface charge method (FSCM) is used for the calculation of the induced electrical field in magnetic stimulation. The method was embedded and optimized in Python. It was designed to allow for the computation of large problems. An element-wise Jacobi method was combined with vectorized matrix operations to increase the parallelization capabilities and enable GPU computing. The induced fields are compared against an analytical solution for a homogeneous sphere and a FEM solution on a realistic head model. The results for both cases show that the normalized root mean square error of less than 0.5% can be achieved with the integral-free FSCM even on low-performance computer hardware.



https://doi.org/10.1109/TMAG.2023.3334747
Zheng, Niannian; Luan, Xiaoli; Shardt, Yuri A. W.; Liu, Fei
Dynamic-controlled principal component analysis for fault detection and automatic recovery. - In: Reliability engineering & system safety, ISSN 1879-0836, Bd. 241 (2024), 109608

To effectively implement the prognostic and health management for industrial processes, a dynamic-controlled principal component analysis (DCPCA) for pattern extraction and deviation diagnosis is proposed under the framework of multivariate statistical modelling, which can accurately detect and automatically rectify the faults. Significantly, the geometric properties of DCPCA are analysed, revealing the spatial structure relationships of different variables and how the data space is partitioned. In addition, the model relationships in DCPCA are explored, including the dynamic characteristics of time-series variables and the algebraic ones of static variables. Based on these results, statistics are derived for monitoring both the dynamic and static relationships of the process, and under the abnormal circumstance, by diagnosing the deviations between the fault pattern and the setpoint, a fault regulator for automatic recovery is designed. The case study of prognostic and health management for an industrial distillation column illustrates the advantages of DCPCA in fully extracting the process dynamics into pattern, as well as fault detection and automatic recovery.



https://doi.org/10.1016/j.ress.2023.109608
Tomova, Mihaela; Hofmann, Martin; Hütterer, Constantin; Mäder, Patrick
Assessing the utility of text-to-SQL approaches for satisfying software developer information needs. - In: Empirical software engineering, ISSN 1573-7616, Bd. 29 (2024), 1, 15, S. 1-48

Software analytics integrated with complex databases can deliver project intelligence into the hands of software engineering (SE) experts for satisfying their information needs. A new and promising machine learning technique known as text-to-SQL automatically extracts information for users of complex databases without the need to fully understand the database structure nor the accompanying query language. Users pose their request as so-called natural language utterance, i.e., question. Our goal was evaluating the performance and applicability of text-to-SQL approaches on data derived from tools typically used in the workflow of software engineers for satisfying their information needs. We carefully selected and discussed five seminal as well as state-of-the-art text-to-SQL approaches and conducted a comparative assessment using the large-scale, cross-domain Spider dataset and the SE domain-specific SEOSS-Queries dataset. Furthermore, we study via a survey how SE professionals perform in satisfying their information needs and how they perceive text-to-SQL approaches. For the best performing approach, we observe a high accuracy of 94% in query prediction when training specifically on SE data. This accuracy is almost independent of the query’s complexity. At the same time, we observe that SE professionals have substantial deficits in satisfying their information needs directly via SQL queries. Furthermore, SE professionals are open for utilizing text-to-SQL approaches in their daily work, considering them less time-consuming and helpful. We conclude that state-of-the-art text-to-SQL approaches are applicable in SE practice for day-to-day information needs.



https://doi.org/10.1007/s10664-023-10374-z
Schmidt, Sascha; Zimmermann, Armin
Model-based reliability engineering of automotive drivetrain architectures with multi-trajectory simulation. - In: 16th International Probabilistic Safety Assessment and Management Conference 2022 (PSAM 16), (2023), S. 167-179

The reliability of safety-critical systems needs to be evaluated during their design. Model-based systems engineering methods and tools for stochastic discrete-event systems support this with domain-specific models as well as simulation and analysis algorithms for complex dynamic systems. However, reliability models can become computationally expensive to evaluate, either because the state space of the systems is large or if rare events are important. The paper introduces a modular model to evaluate the reliability of fault-tolerant automotive drivetrain architectures for future electric autonomous cabs. We show how the recently proposed multi-trajectory simulation method can solve such problems efficiently, by integrating the advantages of simulation and numerical analysis.



Walter, Uwe; Brandt, Stephan A.; Förderreuther, Stefanie; Günther, Albrecht; Hansen, Hans-Christian; Haueisen, Jens; Salih, Farid; Weise, David
Recommendations of the German Society for Clinical Neurophysiology and Functional Imaging for the diagnosis of irreversible loss of brain function :
Empfehlungen der Deutschen Gesellschaft für Klinische Neurophysiologie und Funktionelle Bildgebung (DGKN) zur Diagnostik des irreversiblen Hirnfunktionsausfalls. - In: Klinische Neurophysiologie, ISSN 1439-4081, Bd. 54 (2023), 4, S. 221-230

Im Zuge der Fünften Fortschreibung der Richtlinie der Bundesärztekammer zur Feststellung des irreversiblen Hirnfunktionsausfalls, gültig seit September 2022, wurden die Empfehlungen der Deutschen Gesellschaft für Klinische Neurophysiologie und Funktionelle Bildgebung (DGKN) zu den ergänzenden elektrophysiologischen und Doppler-sonografischen Untersuchungen aktualisiert. Die hier präsentierten Empfehlungen der DGKN beinhalten die Indikationen, Voraussetzungen und die Durchführung dieser Zusatzverfahren bei Kindern und Erwachsenen.



https://doi.org/10.1055/a-2069-3379