Article
Refine
Year of publication
Document Type
- Article (1840) (remove)
Is part of the Bibliography
- no (1840)
Keywords
- Simulation (17)
- Politische Bildung (15)
- Betriebliches Informationssystem (14)
- BEHAVIOR (13)
- Gebärmutterhalskrebs (12)
- Humans (12)
- Digitalisierung (11)
- Bildgebendes Verfahren (10)
- Biomechanics (10)
- Maschinelles Lernen (10)
Institute
- Fakultät Angewandte Sozial- und Gesundheitswissenschaften (461)
- Fakultät Informatik und Mathematik (399)
- Fakultät Maschinenbau (361)
- Fakultät Elektro- und Informationstechnik (244)
- Fakultät Angewandte Natur- und Kulturwissenschaften (171)
- Institut für Sozialforschung und Technikfolgenabschätzung (IST) (128)
- Labor für Technikfolgenabschätzung und Angewandte Ethik (LaTe) (118)
- Regensburg Center of Health Sciences and Technology - RCHST (104)
- Fakultät Bauingenieurwesen (100)
- Labor Empirische Sozialforschung (94)
Begutachtungsstatus
- peer-reviewed (953)
- begutachtet (43)
Context:
Causal probabilistic graph-based models have gained widespread utility, enabling the modeling of cause-and-effect relationships across diverse domains. With their rising adoption in new areas, such as safety analysis of complex systems, software engineering, and machine learning, the need for an integrated lifecycle framework akin to DevOps and MLOps has emerged. Currently, such a reference for organizations interested in employing causal engineering is missing. This lack of guidance hinders the incorporation and maturation of causal methods in the context of real-life applications.
Objective:
This work contextualizes causal model usage across different stages and stakeholders and outlines a holistic view of creating and maintaining them within the process landscape of an organization.
Method:
A novel lifecycle framework for causal model development and application called CausalOps is proposed. By defining key entities, dependencies, and intermediate artifacts generated during causal engineering, a consistent vocabulary and workflow model to guide organizations in adopting causal methods are established.
Results:
Based on the early adoption of the discussed methodology to a real-life problem within the automotive domain, an experience report underlining the practicability and challenges of the proposed approach is discussed.
Conclusion:
It is concluded that besides current technical advancements in various aspects of causal engineering, an overarching lifecycle framework that integrates these methods into organizational practices is missing. Although diverse skills from adjacent disciplines are widely available, guidance on how to transfer these assets into causality-driven practices still need to be addressed in the published literature. CausalOps’ aim is to set a baseline for the adoption of causal methods in practical applications within interested organizations and the causality community.
Interpretable Machine Learning for Mode Choice Modeling on Tracking-Based Revealed Preference Data
(2024)
Mode choice modeling is imperative for predicting and understanding travel behavior. For this purpose, machine learning (ML) models have increasingly been applied to stated preference and traditional self-recorded revealed preference data with promising results, particularly for extreme gradient boosting (XGBoost) and random forest (RF) models. Because of the rise in the use of tracking-based smartphone applications for recording travel behavior, we address the important and unprecedented task of testing these ML models for mode choice modeling on such data. Furthermore, as ML approaches are still criticized for leading to results that are hard to understand, we consider it essential to provide an in-depth interpretability analysis of the best-performing model. Our results show that the XGBoost and RF models far outperform a conventional multinomial logit model, both overall and for each mode. The interpretability analysis using the Shapley additive explanations approach reveals that the XGBoost model can be explained well at the overall and mode level. In addition, we demonstrate how to analyze individual predictions. Lastly, a sensitivity analysis gives insight into the relative importance of different data sources, sample size, and user involvement. We conclude that the XGBoost model performs best, while also being explainable. Insights generated by such models can be used, for instance, to predict mode choice decisions for arbitrary origin–destination pairs to see which impacts infrastructural changes would have on the mode share.
In a number of tomographic applications, data cannot be fully acquired, resulting in severely underdetermined image reconstruction. Conventional methods in such cases lead to reconstructions with significant artifacts. To overcome these artifacts, regularization methods are applied that incorporate additional information. An important example is TV reconstruction, which is known to be efficient in compensating for missing data and reducing reconstruction artifacts. On the other hand, tomographic data are also contaminated by noise, which poses an additional challenge. The use of a single regularizer must therefore account for both the missing data and the noise. A particular regularizer may not be ideal for both tasks. For example, the TV regularizer is a poor choice for noise reduction over multiple scales, in which case ℓ1 curvelet regularization methods are well suited. To address this issue, in this paper, we present a novel variational regularization framework that combines the advantages of different regularizers. The basic idea of our framework is to perform reconstruction in two stages. The first stage is mainly aimed at accurate reconstruction in the presence of noise, and the second stage is aimed at artifact reduction. Both reconstruction stages are connected by a data proximity condition. The proposed method is implemented and tested for limited-view CT using a combined curvelet–TV approach. We define and implement a curvelet transform adapted to the limited-view problem and illustrate the advantages of our approach in numerical experiments.
An electric traction machine for an electric or a hybrid electric vehicle is usually designed for a specific operating point or cycle. For such an operating point or cycle, the masses and the cooling circuit of the electric machine determine the time dependent temperature distribution within the machine. For a specific load cycle, the thermal simulation of the machine can reveal possible mass and size reductions for a given insulation class of the machine. In addition, such simulations allow the comparison of various cooling concepts. In the machine design process, the first step is a conventional electromagnetic machine design. From the geometric data of this design and the material properties, the parameters of a thermal equivalent circuit can be derived. The differential and algebraic equations of the thermal equivalent circuit are solved by a simulation tool to predict the temperatures of the critical parts in the electric machine. A thermal equivalent circuit is accurate enough to predict the thermal behavior of the critical parts in the electric machine, and yet not too complex, to obtain simulation results with moderate numerical effort. This enables an iterative design process to optimize the drive.
AbstractTensegrity structures are prestressed structures consisting of compressed members connected by prestressed tensioned members. Due to their properties, such as flexibility and lightness, mobile robots based on these structures are an attractive subject of research and are suitable for space applications. In this work, a mobile robot based on a tensegrity structure with two curved members connected by eight tensioned strings is analyzed in terms of deformation in the curved members. Further, the difference in locomotion trajectory between the undeformed and deformed structure after the prestress is analyzed. For that, the theory of large deflections of rod-like structures is used. To determine the relationship between acting forces and the deformation, the structure is optimized using minimization algorithms in Python. The results are validated by parameter studies in FEM. The analysis shows that the distance between the two curved members significantly influences the structure’s locomotion. It can be said that the deformation of the components significantly influences the locomotion of tensegrity structures and should be considered when analyzing highly compliant structures.
Operational wear behaviour of 3D-printed lightweight metal gears: EDS and oil analysis comparison
(2024)
Additive manufacturing (AM) has come to the fore in recent years among manufacturing techniques. This technique, which has different advantages than traditional ones such as casting, forging and machining, is expected to be widely used in producing machine parts like gears in the coming years. Therefore, experimental data on AM parameters for lightweight metal gears are important for industrial production. In this study, a wear test was applied to AlSi10Mg and Ti6Al4V gears under operational conditions, and the wear behaviour of conventionally and additively manufactured gears was compared. The amount of abrasion elements was determined by analysing the oil in the gearbox. In addition, gear surfaces were analysed using scanning electron microscopy and an energy-dispersive spectrometer before and after wear. Thus, the wear behaviour of gears produced by conventional and AM under service conditions was demonstrated comparatively.
Using markerless motion capture and musculoskeletal models: An evaluation of joint kinematics
(2024)
BACKGROUND: This study presents a comprehensive comparison between a marker-based motion capture system (MMC) and a video-based motion capture system (VMC) in the context of kinematic analysis using musculoskeletal models. OBJECTIVE: Focusing on joint angles, the study aimed to evaluate the accuracy of VMC as a viable alternative for biomechanical research.
METHODS: Eighteen healthy subjects performed isolated movements with 17 joint degrees of freedom, and their kinematic data were collected using both an MMC and a VMC setup. The kinematic data were entered into the AnyBody Modelling System, which enables the calculation of joint angles. The mean absolute error (MAE) was calculated to quantify the deviations between the two systems.
RESULTS: The results showed good agreement between VMC and MMC at several joint angles. In particular, the shoulder, hip and knee joints showed small deviations in kinematics with MAE values of 4.8∘, 6.8∘ and 3.5∘, respectively. However, the study revealed problems in tracking hand and elbow movements, resulting in higher MAE values of 13.7∘ and 27.7∘. Deviations were also higher for head and thoracic movements.
CONCLUSION: Overall, VMC showed promising results for lower body and shoulder kinematics. However, the tracking of the wrist and pelvis still needs to be refined. The research results provide a basis for further investigations that promote the fusion of VMC and musculoskeletal models.
Pelvic floor dysfunction is a common problem in women and has a negative impact on their quality of life. The aim of this review was to provide a general overview of the current state of technology used to assess pelvic floor functionality. It also provides literature research of the physiological and anatomical factors that correlate with pelvic floor health. This systematic review was conducted according to the PRISMA guidelines. The PubMed, ScienceDirect, Cochrane Library, and IEEE databases were searched for publications on sensor technology for the assessment of pelvic floor functionality. Anatomical and physiological parameters were identified through a manual search. In the systematic review, 114 publications were included. Twelve different sensor technologies were identified. Information on the obtained parameters, sensor position, test activities, and subject characteristics was prepared in tabular form from each publication. A total of 16 anatomical and physiological parameters influencing pelvic floor health were identified in 17 published studies and ranked for their statistical significance. Taken together, this review could serve as a basis for the development of novel sensors which could allow for quantifiable prevention and diagnosis, as well as particularized documentation of rehabilitation processes related to pelvic floor dysfunctions.
Extracorporeal membrane oxygenation (ECMO) was established as a treatment for severe cardiac or respiratory disease. Intra-device clot formation is a common risk. This is based on complex coagulation phenomena which are not yet sufficiently understood. The objective was the development and validation of a methodology to capture the key properties of clots deposed in membrane lungs (MLs), such as clot size, distribution, burden, and composition. One end-oftherapy PLS ML was examined. Clot detection was performed using multidetector computed tomography (MDCT), microcomputed tomography (μCT), and photography of fiber mats (fiber mat imaging, FMI). Histological staining was conducted for von Willebrand factor (vWF), platelets (CD42b, CD62P), fibrin, and nucleated cells (4′, 6-diamidino-2-phenylindole, DAPI). The three imaging
methods showed similar clot distribution inside the ML. Independent of the imaging method, clot loading was detected predominantly in the inlet chamber of the ML. The μCT had the highest accuracy. However, it was more expensive and time consuming than MDCT or FMI. The MDCT detected the clots with low scanning time. Due to its lower resolution, it only showed clotted areas but not
the exact shape of clot structures. FMI represented the simplest variant, requiring little effort and resources. FMI allowed clot localization and calculation of clot volume. Histological evaluation indicated omnipresent immunological deposits throughout the ML. Visually clot-free areas were covered with leukocytes and platelets forming platelet-leukocyte aggregates (PLAs). Cells were embedded in vWF cobwebs, while vWF fibers were negligible. In conclusion, the presented
methodology allowed adequate clot identification and histological classification
of possible thrombosis markers such as PLAs.
The recent REACH regulations require the elimination of bisphenol-A and titanium dioxide from commercially available boron-based polymers. This has led to changes in some of the mechanical characteristics, which strongly influence the properties of magnetoactive borosilicate polymers. This work delivers results on the electrical properties and discusses some implications for future research using bisphenol-A and titanium-dioxide-free substitutes.