Informatik
Refine
Year of publication
- 2023 (5) (remove)
Document Type
- Master's Thesis (5)
Has Fulltext
- yes (5)
Is part of the Bibliography
- no (5)
Keywords
- Antragswesen (1)
- Big Data Architektur (1)
- Blockchain (1)
- Circular Economy (1)
- DLT (1)
- Distributed Ledger Technology (1)
- Ethikantrag (1)
- Forschungsethik (1)
- Hyperledger Fabric (1)
- Industriell Engineering (1)
Die aktuell eingesetzte Word-Antragsvorlage zur Erstellung und Einreichung von Ethikanträgen für die Forschungsethik-Kommission der Fachhochschule Vorarlberg entspricht nicht mehr den Anforderungen und Wünschen der Anwender:innen. Neben technischen Limitierungen stellen vor allem die eingeschränkten Möglichkeiten den Grund dar, weshalb anhand der Prinzipien sowie der vier Phasen des User-Centered Designs eine Software-Lösung ausgearbeitet wurde, die das bestehende System langfristig ablösen können soll. Die einzelnen Kapitel dieser Arbeit entsprechen jeweils einer der vier Phasen und stellen als Ganzes eine vollständig abgeschlossene Iteration des Prozesses dar. Die durchgeführte Analyse der Nutzerkontexte basiert auf der Aufarbeitung der Kriterien der Forschungsethik und vor allem einer detaillierten Analyse des bestehenden Systems und Prozesses der Forschungsethik-Kommission. Neben der Funktionsweise und dem inhaltlichen Aufbau werden im Zuge dessen gleichzeitig die Stärken und Schwächen erläutert. Ebenso findet eine breite Analyse von anderweitigen Systemen und Prozessen von Ethikkommissionen innerhalb Österreichs statt, um den Stand der Technik zu erheben. Die anschließende Definition von konkreten Anforderungen basiert auf der Umsetzung einer qualitativen Inhaltsanalyse der durchgeführten Interviews nach Kuckartz. Insgesamt können dabei neun Anforderungen definiert werden, die unterschiedlich priorisiert größtenteils in der eigens entwickelten EthicsVision Plattform umgesetzt werden konnten. Zum Einsatz kommen dabei sowohl Docker als auch das Symfony-Framework und die Angular-Plattform. Die abschließende Evaluation des Prototyps basiert auf Feedback der Antragsteller:innen und der Forschungsethik-Kommission. Der Proof of Concept wird dabei als gute Basis wahrgenommen, während bereits diverse Weiterentwicklungsmöglichkeiten aufgezeigt werden können. Das Ziel der Arbeit, die Neuentwicklung des Ethikantrag-Tools zu bewerkstelligen, konnte erreicht und ein Weg für die künftige Gestaltung des Prozesses aufgezeigt werden.
This thesis evaluates the feasibility of conducting visual inspection tests on power industry constructions using object detection techniques. The introduction provides an overview of this field’s state-of-the-art technologies and approaches. For the implementation, a case study is then conducted, which is done in collaboration with the partner company OMICRON Electronics GmbH, focusing on power transformers as an example. The objective is to develop an inspection test using photographs to identify power transformers and their subcomponents and detect existing rust spots and oil leaks within these components. Three object detection models are trained: one for power transformers and sub-components, one for rust detection, and one for oil leak detection. The training process utilizes the implementation of the YOLOv5 algorithm on a Linux-based workstation with an NVIDIA Quadro RTX 4000 GPU. The power transformer model is trained on a dataset provided by the partner company, while open-source datasets are used for rust and oil leak detection. The study highlights the need for a more powerful GPU to enhance training experiments and utilizes an Azure DevOps Pipeline to optimize the workflow. The performance of the power transformer detection model is satisfactory but influenced by image angles and an imbalance of certain sub-components in the dataset. Multi-angle video footage is a proposed solution for the inspection test to address this limitation and increase the size of the dataset, focusing on reducing the imbalance. The models trained on open-source datasets demonstrate the potential for rust and oil leak detection but lack accuracy due to their generic nature. Therefore, the datasets must be adjusted with case-specific data to achieve the desired accuracy for reliable visual inspection tests. The results of the case study have been well-received by the partner company’s management, indicating future development opportunities. This case study will likely be a foundation for implementing visual inspection tests as a product.
Lack of transparency and traceability of products and their raw materials means that most products can only be thrown away or not properly recycled due to a lack of relevant data. This conflicts with the circular economy principles, which are demanded by several initiatives, including the European Union. The aim of this master thesis is to analyze this conflict and to propose a technical solution based on Distributed Ledger Technology that enables transparency and traceability of products and their materials. Therefore, the thesis addresses two central research questions: 1. How can traceability and transparency be enabled by integrating a DLT solution? 2. How would a prototype with the integration of smart contracts and DLT look like? To answer these questions, a blockchain solution is implemented using Hyperledger Fabric. The solution uses the immutability and decentralized nature of DLT to record and track the movement of products and their materials throughout their life cycle in the Circular Economy. Furthermore, with private data collections, confidentiality, and privacy are granted while ensuring transparency. The thesis contributes to the Circular Economy field by exploring the principles, models, and challenges of the Circular Economy and the circularity goals of a Digital Product Passport to develop a suitable technical solution. The chosen blockchain framework, Hyperledger Fabric, is presented, and its key components and features are highlighted. The thesis also delves into the design decisions and considerations behind the Digital Product Passport platform, explaining the architecture and transaction flow together with the prototype implementation and demonstration to showcase the functionality of the solution. Results and analysis provide insights into the challenges of the Circular Economy, sustainable resource management, and the Digital Product Passport, resulting in recommendations for future improvements and enhancements. Overall, this thesis offers a practical solution utilizing DLT to enable transparency and traceability in the Circular Economy, contributing to the realization of sustainable and efficient resource management practices to ultimately contribute to the set Circular Economy initiatives.
Programmable Logic Controller (PLC) modules are used in industrial settings to control and monitor various manufacturing processes. Detecting these modules can be helpful during installation and maintenance. However, the limited availability of real annotated images to train an object detector poses a challenge. This thesis aims to research object detection of these modules on real images by using synthetic data during training. The synthetic images are generated from CAD models and improved with Generative Adversarial Networks (GANs). The CAD models are rendered in different scenes, and perfectly annotated images are automatically saved. A technique called domain randomization is applied during rendering. It renders the modules in different poses with constantly changing backgrounds. As the CAD models do not visually resemble the real modules, it is necessary to improve the synthetic images. This project researches StarGAN and CycleGAN for the task of image-to-image translation. A GAN is trained with real and synthetic images and can then translate between these domains. YOLOv8 and Faster R-CNN are tested for object detection. The best mean Average Precision (mAP) is achieved when training with a synthetic dataset where 50% of the images were improved with StarGAN. When trained with YOLOv8 and evaluated on a real dataset, it achieves a mAP of 84.4%. Overall, the accuracy depends on the quality of the CAD models. Using a GAN improves the detection rate for all modules, but especially for unrealistic CAD models.