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Decubitus is one of the most relevant diseases in nursing and the most expensive to treat. It is caused by sustained pressure on tissue, so it particularly affects bed-bound patients. This work lays a foundation for pressure mattress-based decubitus prophylaxis by implementing a solution to the single-frame 2D Human Pose Estimation problem.
For this, methods of Deep Learning are employed. Two approaches are examined, a coarse-to-fine Convolutional Neural Network for direct regression of joint coordinates and a U-Net for the derivation of probability distribution heatmaps.
We conclude that training our models on a combined dataset of the publicly available Bodies at Rest and SLP data yields the best results. Furthermore, various preprocessing techniques are investigated, and a hyperparameter optimization is performed to discover an improved model architecture.
Another finding indicates that the heatmap-based approach outperforms direct regression.
This model achieves a mean per-joint position error of 9.11 cm for the Bodies at Rest data and 7.43 cm for the SLP data.
We find that it generalizes well on data from mattresses other than those seen during training but has difficulties detecting the arms correctly.
Additionally, we give a brief overview of the medical data annotation tool annoto we developed in the bachelor project and furthermore conclude that the Scrum framework and agile practices enhanced our development workflow.
RailChain
(2023)
The RailChain project designed, implemented, and experimentally evaluated a juridical recorder that is based on a distributed consensus protocol. That juridical blockchain recorder has been realized as distributed ledger on board the advanced TrainLab (ICE-TD 605 017) of Deutsche Bahn.
For the project, a consortium consisting of DB Systel, Siemens, Siemens Mobility, the Hasso Plattner Institute for Digital Engineering, Technische Universität Braunschweig, TÜV Rheinland InterTraffic, and Spherity has been formed. These partners not only concentrated competencies in railway operation, computer science, regulation, and approval, but also combined experiences from industry, research from academia, and enthusiasm from startups.
Distributed ledger technologies (DLTs) define distributed databases and express a digital protocol for transactions between business partners without the need for a trusted intermediary. The implementation of a blockchain with real-time requirements for the local network of a railway system (e.g., interlocking or train) allows to log data in the distributed system verifiably in real-time. For this, railway-specific assumptions can be leveraged to make modifications to standard blockchains protocols.
EULYNX and OCORA (Open CCS On-board Reference Architecture) are parts of a future European reference architecture for control command and signalling (CCS, Reference CCS Architecture – RCA). Both architectural concepts outline heterogeneous IT systems with components from multiple manufacturers. Such systems introduce novel challenges for the approved and safety-relevant CCS of railways which were considered neither for road-side nor for on-board systems so far. Logging implementations, such as the common juridical recorder on vehicles, can no longer be realized as a central component of a single manufacturer. All centralized approaches are in question.
The research project RailChain is funded by the mFUND program and gives practical evidence that distributed consensus protocols are a proper means to immutably (for legal purposes) store state information of many system components from multiple manufacturers. The results of RailChain have been published, prototypically implemented, and experimentally evaluated in large-scale field tests on the advanced TrainLab. At the same time, the project showed how RailChain can be integrated into the road-side and on-board architecture given by OCORA and EULYNX.
Logged data can now be analysed sooner and also their trustworthiness is being increased. This enables, e.g., auditable predictive maintenance, because it is ensured that data is authentic and unmodified at any point in time.
Like conventional software projects, projects in model-driven software engineering require adequate management of multiple versions of development artifacts, importantly allowing living with temporary inconsistencies. In the case of model-driven software engineering, employed versioning approaches also have to handle situations where different artifacts, that is, different models, are linked via automatic model transformations.
In this report, we propose a technique for jointly handling the transformation of multiple versions of a source model into corresponding versions of a target model, which enables the use of a more compact representation that may afford improved execution time of both the transformation and further analysis operations. Our approach is based on the well-known formalism of triple graph grammars and a previously introduced encoding of model version histories called multi-version models. In addition to showing the correctness of our approach with respect to the standard semantics of triple graph grammars, we conduct an empirical evaluation that demonstrates the potential benefit regarding execution time performance.
Modular and incremental global model management with extended generalized discrimination networks
(2023)
Complex projects developed under the model-driven engineering paradigm nowadays often involve several interrelated models, which are automatically processed via a multitude of model operations. Modular and incremental construction and execution of such networks of models and model operations are required to accommodate efficient development with potentially large-scale models. The underlying problem is also called Global Model Management.
In this report, we propose an approach to modular and incremental Global Model Management via an extension to the existing technique of Generalized Discrimination Networks (GDNs). In addition to further generalizing the notion of query operations employed in GDNs, we adapt the previously query-only mechanism to operations with side effects to integrate model transformation and model synchronization. We provide incremental algorithms for the execution of the resulting extended Generalized Discrimination Networks (eGDNs), as well as a prototypical implementation for a number of example eGDN operations.
Based on this prototypical implementation, we experiment with an application scenario from the software development domain to empirically evaluate our approach with respect to scalability and conceptually demonstrate its applicability in a typical scenario. Initial results confirm that the presented approach can indeed be employed to realize efficient Global Model Management in the considered scenario.