05 Fakultät Informatik, Elektrotechnik und Informationstechnik
Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/6
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Item Open Access Modeling and timing analysis of micro-ROS application on an off-road vehicle control unit(2022) Bappanadu, Suraj RaoROS is known to be the most popular middleware for the development of software in modern day robots. It's next version, ROS 2 is highly modular and offers flexibility by supporting on microprocessors running desktop operating systems. Micro-ROS puts the major ROS 2 features on microcontrollers, i.e., highly resource-constrained computing devices running specialized real-time operating systems. ROS 2 is also of great importance for other domains, including autonomous driving and the off-road sector. Accordingly, there is significant interest in bringing micro-ROS to typical automotive control units. These embedded platforms support AUTOSAR Classic OSEK-like operating system which is very different in many aspects when compared to the platforms supported by micro-ROS. Some of the aspects have already been addressed in a previous work. This thesis mainly focuses on mapping the micro-ROS execution scheme to AUTOSAR scheme and dynamic memory management of the micro-ROS stack. From the micro-ROS architecture perspective, to successfully port the stack on an AUTOSAR-based ECU, the middleware and other layers of the stack are also analysed and adapted using a standard approach to support tasks-like execution model instead of threads-like execution model. Additionally, the support for standard CAN protocol based on custom transport configuration with the hardware CAN on the BODAS ECU is introduced. Model-based development methods have proven their utility in automotive industry. Therefore, we also focus on describing the timing properties of the micro-ROS stack in a model-based approach. We develop a generic model which is independent of a specific modeling language. In the next step, we realize the generic model using the widely used AMALTHEA language and analyse how well the developed model predicts the timing behavior of micro-ROS tasks. Finally, the effectiveness of the approach regarding timing and modeling is demonstrated with a micro-ROS test application first on Linux and then on the off-road vehicle control unit BODAS RC18-12/40 by Bosch Rexroth.Item Open Access Industry practices and challenges of using AI planning : an interview-based study(2024) Vashisth, DhananjayIn the rapidly evolving landscape of industrial applications, AI planning systems have emerged as critical tools for optimizing processes and decision-making. However, implementing and integrating these systems present significant challenges that can hinder their effectiveness. This thesis addresses the urgent need to understand the best practices and challenges involved in designing, integrating, and deploying AI planning systems in industrial settings. Without this understanding, industries risk inefficient implementation, leading to poor performance and resistance from end-users. This research employs a methodology that includes a literature review and interviews with industry professionals and researchers to identify common strategies and obstacles practitioners face. The study examines existing literature to uncover reported best practices and challenges in AI planning systems. Interviews provide additional perspectives, enriching the data collected and ensuring a thorough analysis. The findings reveal best practices, including the importance of cross-disciplinary collaboration, robust data management strategies, and iterative development processes. Additionally, recurring challenges such as integration complexities, scalability issues, and the need for continuous system evaluation are identified. These insights highlight critical areas for improvement and offer practical recommendations for enhancing the effectiveness of AI planning systems in industrial applications.Item Open Access Feasibility analysis of using Model Predictive Control in Demand-Side Management of residential building(2020) Ramachandran Selvaraj, Sri VishnuThe energy systems are becoming smart recently with an increase in communication capabilities between producer, distributor and consumer. Also, many distributed renewable energy producers both in large and domestic scale are adding to the system day by day. Executing Smart Demand-Side Management (DSM) programs can help in providing financial benefits and stability of the energy system without compromising the comfort of end-users. Model Predictive Control (MPC) is an advanced method of process control that is used to control a process while satisfying a set of constraints. Due to its ability to predict future events and generate optimal control, it is widely used in process industries since the 1980s and in recent years it is introduced in power systems. This motivates to study the economic feasibility of using MPC in executing DSM for Residential building, to optimize the power consumption costs and stability of the energy system in the presence of local renewable energy sources (E.g., PV system). The main contribution of this thesis work is to measure the economic benefit of using MPC on DSM of household electricity consumption. A detailed study of modeling the demand side, i.e the appliances of a smart home, along with the domestic energy generators is done in the initial part. Apart from the physical properties of the renewable energy generators, the influence of external factors like weather, dynamic-pricing of electricity and changing user preference is also considered in the model. This formulated model is used to perform simulation of the residential building to generate an optimized energy consumption schedule and calculate the resulting economic benefits. The periodic changes in weather forecast and dynamic-prices are fed into the simulation to improve the prediction accuracy of the system. Lastly, the model is evaluated on a physical implementation to analyze its performance. There are multiple findings as part of the result of this thesis, like the economic benefit of using such a system will encourage many users to participate in Demand response programs, this in turn will help in the reduction of pollution originating from non-renewable energy generators.Item Open Access Design of a software architecture for supervisor system in a satellite(2020) Kelapure, SarthakInternet of Things (IoT) is not just a word now. With an estimated 30 billion devices in the world by 2020, IoT has already become what it was envisioned when the trend began. But there is still a hustle from companies around the world for better and better user experience because the technology keeps getting upgraded and need for upgrade never stops for the user. Researchers and scientists are trying every day to improve the experience by improving the involved things and by improving the communication means, ”the internet”. One such means of communication expected to grow in the future is satellite communication for IoT. Satellite to be used for this purpose needs to be low-cost, robust, reliable, and future ready. An improvement in satellite architecture is imminent. For making satellite feature rich and robust but still low-cost means increase in mission-life of a satellite. Like human life, this can be achieved by better medical system for the satellites. With introduction of a doctor on board, the thesis aims to propose solution for improved mission-life and features for the satellite. The doctor on board in this case is called Supervisor system. This system will need to have a robust and modular software on its designated hardware. Software can be designed and developed to be robust using a standard software architecture that is promising while complementing the requirements. The thesis focuses on designing software architecture for this ”Supervisor system”. By the end of this thesis, the author designs a software architecture for the said system after study of similar architectures. The software architecture is used to develop important features of the mission and is tested for its portability and modularity. Future needs and changes to the existing system are also foreseen and discussed in the end.Item Open Access Economic feasibility analysis of vehicle-to-grid service from an EV owner's perspective in the german electricity market(2020) Malya, Prasad PrakashThe increasing number of Electrical Vehicles (EV) has led to a tremendous amount of inac- cessible electric energy stored in the EV batteries. Vehicle-to-grid (V2G) services can utilize this energy to profit the EV owners’ and stabilize the grid during faults and fluctuations. This thesis presents a novel way of estimating the profitability of V2G from the EV owner’s perspective. The main contribution of this thesis is the formulation of a profit model that includes the EV battery degradation due to V2G. The work done so far considers fixed battery degradation cost, whereas in this work, an online battery degradation model is used. This model takes into account the parameters that represent real-life scenarios resulting in more accurate battery degradation estimation. The V2G profit model uses the electricity price signal from the German energy market for the year 2019 and estimates the annual profit. The first part of the thesis calculates the profitability of V2G, where EV can participate freely in energy arbitrage. This analysis explores the range of profit when EV participates in V2G purely based on the EV owner’s discretion. A sensitivity analysis is done with respect to battery capacity, battery efficiency, and driving distance. The second part of the thesis evaluates the profitability of EV participating in the German energy market’s frequency regulation ancillary service.=. The analysis compares the profitability of EV participating in primary, secondary, and tertiary frequency regulation services. The results of this thesis provide several findings, the potential profit from V2G services should encourage EV owners’ to participate in the V2G services. Additionally, participating in V2G service can extend the life of the battery. However, this depends on the battery technology and battery usage during V2G services. Ancillary services provide higher potential profit compared to energy arbitrage because of the high remuneration scheme. The ancillary services with both capacity and energy payment result in higher profit compared to ancillary services with only capacity payment.Item Open Access Ereignisbasierte Architektur für Quantenanwendungen(2021) Basaric, StefanIm Vergleich zu herkömmlichen Rechnern können mithilfe von Quantencomputern zum ersten Mal komplexe Probleme mit akzeptablen Berechnungszeiten gelöst werden. Diese werden heutzutage durch eine Vielzahl von öffentlichen Cloud-Diensten wie IBM Quantum, Amazon Braket oder Azure Quantum registrierten Nutzern verfügbar gemacht. Um ihre Experimente auf Quantencomputern durchführen zu können, müssen Nutzer Quantenschaltungen schreiben und an die von den Cloud-Diensten bereitgestellten Schnittstellen schicken. Die Quantenschaltungen kommen dabei zunächst in eine Warteschlange, bevor sie schließlich auf dem Quantencomputer ausgeführt werden. Das hat zur Folge, dass die Ausführung im Vergleich zur reinen Berechnungszeit auf dem Quantencomputer sehr lange dauern kann. Die aktuell verfügbaren Cloud-Dienste bieten derzeit keine Möglichkeit, die Quantenanwendungen ihrer Nutzer zu hosten und sie beim Eintritt von Ereignissen automatisch auszuführen. In dieser Arbeit wird ein Konzept für eine ereignisbasierte Architektur vorgestellt, welches die automatisierte Ausführung von Quantenanwendungen beim Eintritt von beliebigen Ereignissen ermöglicht. Zusätzlich wird ein anhand des Konzepts umgesetzter Prototyp präsentiert, welcher mithilfe von IBM Quantum und OpenWhisk die ereignisbasierte Ausführung von Quantenanwendungen trotz einiger Limitationen ermöglicht.Item Open Access Concept and implementation of a TOSCA orchestration engine for edge and IoT infrastructures(2021) Kiefer, LeonReliable and automated management technologies are essential to support the fast growth of Internet of Things (IoT) applications and infrastructures. Manually deploying IoT applications on thousands of devices in a heterogeneous environment is complex, time-consuming, and error-prone. IoT devices are mostly embedded systems which are deployed as edge devices at specific physical locations where they provide their service by interacting with the physical environment and each other. For example, outdoor temperature sensors, traffic sensors on highways, or remote controlled lights. From a technical perspective, this cyber-physical nature of IoT applications is their most valuable but also their most challenging characteristic. To keep up with the proliferation of IoT technologies, as well as the fast growing needs of IoT applications, their development and deployment speed must increase accordingly. Techniques such as DevOps and continuous delivery, which are well-known in the context of cloud applications, are slowly adapted for IoT applications. One challenge of this process is to automate the deployment and management of IoT applications on edge infrastructures. The Topology and Orchestration Specification for Cloud Applications (TOSCA) enables the automated provisioning and management of various kinds of applications. However, its general-purpose modeling language makes it difficult to capture the cyber-physical nature of IoT applications. Existing TOSCA orchestration engines do not account for the low reliability, size, and heterogeneity of IoT infrastructures. To tackle these issues, this work introduces the Reconciliation-based IoT Application Management (RITAM) approach to manage IoT application deployments on IoT and edge infrastructures. It combines domain-specific modeling of IoT infrastructures and general-purpose modeling using TOSCA. To apply the RITAM approach, this work formalizes the Controller and Reconciler Pattern which replaces imperative management workflows with eventually consistent reconciliation. Moreover, the practical feasibility of RITAM is validated using a prototypical implementation.Item Open Access Continuous estimation of energy efficiency for source code in virtual environments(2024) Schulth, Maximilian NiklasThe increasing energy consumption of servers, high-performance computing clusters, and data centers necessitates measures to reduce energy consumption. However, many companies, like TeamViewer, rely on rented infrastructure where direct hardware-level energy management is unavailable. This thesis presents a method for estimating software energy efficiency on virtualized server environments, focusing on optimizing code execution without access to physical hardware metrics. The research addresses several key challenges, including how to measure energy consumption at the function level of a software, simulate realistic and reproducible user loads, and considerations for isolating performance measurements from external influences. Profiling tools were evaluated to measure CPU time, a metric which is correlated with energy consumption. The method was tested in a virtual environment by simulating user loads and measuring the impact of software changes on performance. The results demonstrate that CPU time can provide insights into the performance of a software which correlates with its energy consumption. This work contributes to the field by providing a lightweight method for continuously estimating the energy efficiency during software development and maintenance.Item Open Access Analysis and development of a digital picking system using IoT and Logistic 4.0(2022) Jahagirdar, AniruddhaOrder picking is a crucial step in nearly all distribution networks and has a significant impact on how well warehouses run. Even though the majority of businesses still use manual order picking, development into diverse options for optimizing order picking operations or providing technological support for human order pickers is advancing quickly. A development in the logistics industry known as Logistics 4.0, or Industry 4.0 in logistics sector is taking place; it addresses new goals and the use of technology to handle impending issues in logistics and warehouse operations. More than half of a warehouse’s operational expenses are largely linked to order picking, which is the process of collecting material from the warehouse and delivering it to the packing station. Order picking procedures still primarily rely on manual labour, which notably contributes to the relatively high process costs, despite the prospect of digitising these procedures growing. In this study, a new way is proposed to optimise the manual operations of the paper-based order picking system by integrating the warehouse routing problem with an interactive webpage to give routes for pickers and order data in order to improve productivity and lower picking error rates. To evaluate the current level of knowledge in this area, a thorough literature study is conducted, and potential research areas in order picking are identified. Along with this, various Travelling Salesman Problem (TSP) heuristics are also examined, and thorough cost and performance analyses are done for various TSP heuristics. Additionally, an economic analysis is performed where several factors are taken into account for both the current paper-based system and the recently built digital system. In the end, this thesis work discovers that the digital picking method performs better in terms of worker ergonomics and efficiency than the traditional paper-based approach. Under the general conditions set, this is the only order picking solution that requires no modification of the warehouse’s environment and a minimum of initial investment in infrastructure.Item Open Access Python's dominance in machine learning : unraveling its emergence and exploring the trade-offs of faster alternatives(2024) Youssef, JohnnyThis research investigates the intricate relationship between library optimization and machine learning algorithm performance across Python, Java, C++, and Julia. Through comprehensive benchmarking of widely used libraries, the study reveals that library efficiency often supersedes the inherent characteristics of programming languages in determining execution speed, accuracy, and energy consumption of machine learning models. The findings challenge the conventional wisdom that compiled languages invariably outperform interpreted ones in computational tasks. Notably, Python’s well-optimized libraries, such as Scikit-learn, demonstrate competitive and sometimes superior performance compared to C++ implementations in specific scenarios. This paradigm shift underscores the critical importance of library selection over language choice in optimizing machine learning workflows. The study delves into the nuanced interplay of factors influencing machine learning performance, including execution efficiency, ecosystem richness, and implementation ease. It also examines the impact of Just-In-Time (JIT) compilation in Julia, revealing significant performance enhancements in subsequent runs, which points to its potential in long-running or repetitive tasks. By providing a comprehensive analysis of the performance landscape across different programming languages and libraries, this study offers valuable insights for practitioners and researchers. It enables informed decision-making in selecting optimal tools and languages for specific machine learning applications, considering not only computational efficiency but also broader ecosystem factors and long-term maintainability. Ultimately, this research contributes to a more nuanced understanding of the performance dynamics in machine learning implementations, challenging preconceptions and providing a data-driven foundation for optimizing machine learning workflows across diverse computational environments.