620 Ingenieurwissenschaften und Maschinenbau
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The identification and traceability of assets throughout their lifecycle is an important prerequisite for the efficient management of circular value creation and the reuse of components and products. This is a particular problem in complex and dynamic value chains. This paper examines the challenges of identifiers in circular supply chains and presents current solution approaches for identification systems by the means of the Digital Product Pass and the Asset Administration Shell. Architectures are presented for ensuring consistent identification in a circular economy and effective traceability systems in global supply chains. The paper highlights the need for both the physical identification features and the associated digital identification information to make an object consistently and uniquely identifiable.
The high attrition rate of Small and Medium Enterprises (SMEs) in South Africa, with a staggering 70% ceasing operations within their initial two years, is a pressing concern extensively highlighted in academic literature. This dissertation presents an innovative approach to support the growth and success of these SMEs by holistically addressing the multifaceted challenges they face, such as limited education, restricted financial access, and inadequate management skills. Current interventions often fall short due to their sector-specific focus, lack of adaptability to diverse user contexts, and scalability challenges, further exacerbated by low adoption rates among SMEs.
To bridge the identified gap in existing solutions, the study poses a central research question: How can a configurable, adaptable, and accessible platform be developed to holistically address the challenges faced by South African SMEs, thereby bolstering their prospects for long-term growth and success? The proposed platform is then developed into a prototype, which is validated in real-world use cases across the services, online retail, and subsistence agriculture sectors. The findings from these implementations underscore the platform's potential in facilitating long-term success. This research lays the foundation for further advancements aimed at strengthening the SME sector in South Africa, with the overarching ambition of fostering a vibrant and resilient national economy.
This research introduces five unique contributions. Foremost is the development of a comprehensive set of networked modules, tailored specifically for South African SMEs. These modules holistically address the multifaceted challenges that SMEs encounter. Bolstering this is a novel platform design, informed by a synthesis of insights from earlier research objectives. This design serves as a roadmap for devising solutions essential to the long-term success of SMEs. The third contribution, inherent in the platform design, is the integration of strategic business management systems, machine learning, and digitalisation. This multi-pronged approach, drawing on the core tenets of industrial engineering, has culminated in a platform tailored to augment SME success in South Africa. Furthermore, the establishment of a database backend for operational planning and operations management dispels the conventional complexities SME stakeholders face, facilitating seamless business performance management. Lastly, building on these foundational elements, an automated mechanism for deriving use case-specific KPIs has been introduced. This mechanism leverages the intricate relationships among measures, sensors, and objectives, with machine learning serving as the catalyst for producing KPIs precisely attuned to specific business objectives.
Traditional learning environments are ineffective and inefficient and are failing to adequately equip students and employees with the knowledge and skills required in today’s jobs, let alone prepare them for the jobs of tomorrow. Given the rapidly changing landscapes of technologies and business models, organisations need to be flexible and adaptable to respond to, and even pre-empt future demands. One of the primary shortcomings of existing learning environments is their inflexibility and the ‘one size fits all’ approach followed.
Serious games and game-based learning are widely recognised for their potential in providing more effective learning environments, especially when designed in a personalised, adaptive manner, and are explored in this dissertation. In addition to adapting to the individual traits and preferences of users, games are also highly context dependent. Whilst there is a great deal of literature and documented case studies of game-based learning, most focus only on the implementation of one particular game in a specific context.
Whilst many existing game design models and approaches focus on achieving improved learning outcomes of learners, there is an opportunity to consider the impact of gameplay on other stake- holders and drive the active development of meta-skills in various stakeholders. Bidirectional learning, where learning simultaneously takes place in a two-way direction [295], has great potential and has, to date, not been incorporated in serious game design. By integrating different perspectives and variable scenarios, the dynamic personalisation of learning trajectories may be possible. Serious games offer a potential platform to aggregate learner behaviours and results, and use these to dynamically configure, adjust and tailor the game to individuals and contexts, ultimately providing a learning environment of improved quality, effectiveness and efficiency.
In this dissertation, adaptive, bidirectional games are explored as a means to provide more effective and efficient learning environments for multiple stakeholders. Moreover, an architecture is presented to support the creation of such games for specific scenarios in a faster, more effective and more efficient manner.
Following a research-by-design approach, the architecture is iteratively developed and simultaneously applied in four case studies. Experiences and learnings from each case study are infused into subsequent design iterations of the architecture.
The architecture allows users to explore and exploit the solution space more deliberately and better understand the various functions and the interrelations between them. The flexible and modular structure of the architecture allows users to prioritise functionalities as required in the given scenario. Furthermore, the directional relations between functions can be interpreted and prioritised as needed given the specific context and requirements. The architecture incorporates various stakeholders in the design process, leading to greater transparency and better understanding throughout the process. More importantly, it emphasises bidirectional learning whereby different stakeholders can learn from gameplay and the aggregated results and behaviours of players.
This paper introduces an AI-assisted pattern generator, aimed to simplify garment design by flattening the pattern creation in an automated process from 3D scans for users without knowledge of conventional pattern construction. This garment tool plug-in computerizes the development of scanned persons into 3D shell surface meshes, which are automatically unwrapped into 2D patterns, streamlining the traditionally complex aspects of garment design for novices. The process uses advanced AI algorithms to facilitate the conversion of 3D scans into usable patterns. Machine learning adapts to different garment styles (close-fitting, regular fit and loose-fitting), ensuring a broad applicability, while customization options allow a precise adaption to individual body measurements. This AI-assisted tool enables a wider audience to generate customized garment creation.
In this study, a method for determining the optimal location and orientation of an implantable piezoelectric accelerometer on the short process of the incus is presented. The accelerometer is intended to be used as a replacement for an external microphone to enable totally implantable auditory prostheses. The optimal orientation of the sensor and the best attachment point are determined based on two criteria—maximum pressure sensitivity sum and minimum loudness level sum. The best location is determined to be near the incudomalleolar joint. We find that the angular orientation of the sensor is critical and provide guidelines on that orientation. The method described in this paper can be used to further optimize the design and performance of the accelerometer.
For collision and obstacle avoidance in path planning, robots usually rely on basic 2D cost maps lacking semantic information about detected obstacles. As a result, the robot’s path planning follows an arbitrarily large safety margin around obstacles.We present a risk-aware 2D cost map for robot navigation that effectively mitigates potential risks, enabling the robot to navigate more confidently and efficiently while maintaining a safe distance from obstacles. It uses commonly available RGBD sensors, making it a practical and accessible option for many applications.Our approach employs a CNN to segment object instances into three distinct safety classes based on common characteristics. Using the abstract representation, we generate semantic occupancy grids, which are then inflated based on their safety classification. These semantic occupancy grids are merged into a final risk-aware 2D cost map. We provide feasible real world results.A robot’s path planner can use the merged cost map as a drop-in replacement for standard 2D cost map, enhancing established robot navigation algorithms based on cost maps without altering their fundamental algorithms and enabling risk-aware navigation in the real world.
A simple technique for generating single and multiple beams from antenna arrays is presented. The approach is based on the multistage sequential rotation technique. A new feature in using multistage sequential rotation is provided. It is demonstrated that by applying a controlled second sequential rotation, circularly polarized antenna arrays operating alternately in a single-beam mode M1 and multi-beam mode M2 in the same frequency band can be designed. Proof-of-concept is provided mathematically and through numerical simulations in light of case studies. The approach can not only be applied to large antenna arrays following a modular principle adapted to the array size and needed applications without loss of generality, but it also paves the way for the manufacture of circularly polarized antennas operating alternately or simultaneously in both modes in the same frequency band. In addition, antennas designed using the proposed approach may have a wide range of applications ranging from monopulse radar to antennas for compensation of interference and blockage in dynamic communication environments.
Viele Verteilnetzbetreiber (VNB) betrachten ihre Niederspannungsnetze als Black Box, da es oft an geeigneten Mess- und Überwachungsinstrumenten fehlt, um detaillierte Einblicke zu erhalten. Diese mangelnde Transparenz erschwert eine präzise Netzsteuerung und -optimierung. Das Projekt „rONT-Alternative“ zielt darauf ab, ein Prognosetool zu entwickeln, das den VNB eine umfassende Übersicht über ihre Netze bietet. Durch detaillierte Analysen und präzisiere Vorhersagen sollen die Transparenz erhöht und die Netzverwaltung verbessert werden, insbesondere im Hinblick auf die Integration erneuerbarer Energien.
Um die Klimaneutralität bis 2045 erreichen zu können, sollen Solaranlagen laut dem Erneuerbaren-Energien-Gesetz 2023 zunehmend in die Verteilnetzebene integriert werden. Solaranlagen sind wetterabhängig, tragen daher zu einer fluktuierenden Erzeugung bei. Im Gegensatz zu konventionellen Kraftwerken sind Solaranlagen nur bedingt steuerbar und sehr volatil in der Einspeisung. Aus diesem Grund sehen sich Verteilnetzbetreiber gezwungen, eine aktivere Rolle im Engpassmanagement zu übernehmen. Im Forschungsprojekt „rONT-Alternative“ wurden zwei Lösungen hierfür miteinander verglichen. Dieser Beitrag beinhaltet ein letztes Szenario, sowie eine Zusammenfassung aller Ergebnisse.
Durch das Zusammenspiel von Facharbeitern und Algorithmen in der automatisierten Werkzeugwartung kann die Lebensdauer von Wälzschälwerkzeugen verlängert und die Notwendigkeit manueller Inspektionen reduziert werden. Der Einsatz von Algorithmen, die ausschließlich mit nominalen Daten arbeiten, bietet für Industrieunternehmen erhebliche Vorteile, da nominale Daten weitaus häufiger vorkommen als Anomalien.