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Software development projects often fail because of insufficient code quality. It is now well documented that the task of testing software, for example, is perceived as uninteresting and rather boring, leading to poor software quality and major challenges to software development companies. One promising approach to increase the motivation for considering software quality is the use of gamification. Initial research works already investigated the effects of gamification on software developers and come to promising. Nevertheless, a lack of results from field experiments exists, which motivates the chapter at hand. By conducting a gamification experiment with five student software projects and by interviewing the project members, the chapter provides insights into the changing programming behavior of information systems students when confronted with a leaderboard. The results reveal a motivational effect as well as a reduction of code smells.
The management of knowledge in organizations considers both established long-term processes and cooperation in agile project teams. Since knowledge can be both tacit and explicit, its transfer from the individual to the organizational knowledge base poses a challenge in organizations. This challenge increases when the fluctuation of knowledge carriers is exceptionally high. Especially in large projects in which external consultants are involved, there is a risk that critical, company-relevant knowledge generated in the project will leave the company with the external knowledge carrier and thus be lost. In this paper, we show the advantages of an early warning system for knowledge management to avoid this loss. In particular, the potential of visual analytics in the context of knowledge management systems is presented and discussed. We present a project for the development of a business-critical software system and discuss the first implementations and results.
Reducing poverty, protecting the planet, and improving life on earth for everyone are the essential goals of the "2030 Agenda for Sustainable Development"committed by the United Nations (UN). Achieving those goals will require technological innovation as well as their implementation in almost all areas of our business and day-to-day life. This paper proposes a high-level framework that collects and structures different uses cases addressing the goals defined by the UN. Hence, it contributes to the discussion by proposing technical innovations that can be used to achieve those goals. As an example, the goal "Climate Actionïs discussed in detail by describing use cases related to tackling biodiversity loss in order to conservate ecosystems.
This paper describes the potential for developing a digital twin of society- a dynamic model that can be used to observe, analyze, and predict the evolution of various societal aspects. Such a digital twin can help governmental agencies and policy makers in interpreting trends, understanding challenges, and making decisions regarding investments or policies necessary to support societal development and ensure future prosperity. The paper reviews related work regarding the digital twin paradigm and its applications. The paper presents a motivating case study- an analysis of opportunities and challenges faced by the German federal employment agency, Bundesagentur f¨ur Arbeit (BA), proposes solutions using digital twins, and describes initial proofs of concept for such solutions.
In the context of the increasing digitalization, the Internet of Things (IoT) is seen as a technological driver through which completely new business models can emerge in the interaction of different players. Identified key players include traditional industrial companies, municipalities and telecommunications companies. The latter, by providing connectivity, ensure that small devices with tiny batteries can be connected almost anywhere and directly to the Internet. There are already many IoT use cases on the market that provide simplification for end users, such as Philips Hue Tap. In addition to business models based on connectivity, there is great potential for information-driven business models that can support or enhance existing business models. One example is the IoT use case Park and Joy, which uses sensors to connect parking spaces and inform drivers about available parking spaces in real time. Information-driven business models can be based on data generated in IoT use cases. For example, a telecommunications company can add value by deriving more decision-relevant information – called insights – from data that is used to increase decision agility. In addition, insights can be monetized. The monetization of insights can only be sustainable, if careful attention is taken and frameworks are considered. In this chapter, the concept of information-driven business models is explained and illustrated with the concrete use case Park and Joy. In addition, the benefits, risks and framework conditions are discussed.
A novel method to determine the extruded length of a metallic wire for a directed energy deposition (DED) process using a microwave (MW) plasma jet with a straight-through wire feed is presented. The method is based on the relative comparison of the measured frequency response obtained by the large-signal scattering parameter (Hot-S) technique. In the practical working range, repeatability of less than 6% for a nonactive plasma and 9% for the active plasma state is found. Measurements are conducted with a focus on a simple solution to decrease the processing time and reduce the integration time of the process into the existing hardware. It is shown that monitoring a single frequency for magnitude and phase changes is sufficient to achieve good accuracy. A combination of different measurement values to determine the length is possible. The applicability to different diameter of the same material is shown as well as a contact detection of the wire and metallic substrate.
This article addresses the need for an innovative technique in plasma shaping, utilizing antenna structures, Maxwell’s laws, and boundary conditions within a shielded environment. The motivation lies in exploring a novel approach to efficiently generate high-energy density plasma with potential applications across various fields. Implemented in an E01 circular cavity resonator, the proposed method involves the use of an impedance and field matching device with a coaxial connector and a specially optimized monopole antenna. This setup feeds a low-loss cavity resonator, resulting in a high-energy density air plasma with a surface temperature exceeding 3500 o C, achieved with a minimal power input of 80 W. The argon plasma, resembling the shape of a simple monopole antenna with modeled complex dielectric values, offers a more energy-efficient alternative compared to traditional, power-intensive plasma shaping methods. Simulations using a commercial electromagnetic (EM) solver validate the design’s effectiveness, while experimental validation underscores the method’s feasibility and practical implementation. Analyzing various parameters in an argon atmosphere, including hot S -parameters and plasma beam images, the results demonstrate the successful application of this technique, suggesting its potential in coating, furnace technology, fusion, and spectroscopy applications.
Ga-doped Li7La3Zr2O12 garnet solid electrolytes exhibit the highest Li-ion conductivities among the oxide-type garnet-structured solid electrolytes, but instabilities toward Li metal hamper their practical application. The instabilities have been assigned to direct chemical reactions between LiGaO2 coexisting phases and Li metal by several groups previously. Yet, the understanding of the role of LiGaO2 in the electrochemical cell and its electrochemical properties is still lacking. Here, we are investigating the electrochemical properties of LiGaO2 through electrochemical tests in galvanostatic cells versus Li metal and complementary ex situ studies via confocal Raman microscopy, quantitative phase analysis based on powder X-ray diffraction, energy-dispersive X-ray spectroscopy, X-ray photoelectron spectroscopy, and electron energy loss spectroscopy. The results demonstrate considerable and surprising electrochemical activity, with high reversibility. A three-stage reaction mechanism is derived, including reversible electrochemical reactions that lead to the formation of highly electronically conducting products. The results have considerable implications for the use of Ga-doped Li7La3Zr2O12 electrolytes in all-solid-state Li-metal battery applications and raise the need for advanced materials engineering to realize Ga-doped Li7La3Zr2O12for practical use.
After a brief introduction of conventional laboratory structures, this work focuses on an innovative and universal approach for a setup of a training laboratory for electric machines and drive systems. The novel approach employs a central 48 V DC bus, which forms the backbone of the structure. Several sets of DC machine, asynchronous machine and synchronous machine are connected to this bus. The advantages of the novel system structure are manifold, both from a didactic and a technical point of view: Student groups can work on their own performance level in a highly parallelized and at the same time individualized way. Additional training setups (similar or different) can easily be added. Only the total power dissipation has to be provided, i.e. the DC bus balances the power flow between the student groups. Comparative results of course evaluations of several cohorts of students are shown.
In this paper, the use of reinforcement learning (RL) in control systems is investigated using a rotatory inverted pendulum as an example. The control behavior of an RL controller is compared to that of traditional LQR and MPC controllers. This is done by evaluating their behavior under optimal conditions, their disturbance behavior, their robustness and their development process. All the investigated controllers are developed using MATLAB and the Simulink simulation environment and later deployed to a real pendulum model powered by a Raspberry Pi. The RL algorithm used is Proximal Policy Optimization (PPO). The LQR controller exhibits an easy development process, an average to good control behavior and average to good robustness. A linear MPC controller could show excellent results under optimal operating conditions. However, when subjected to disturbances or deviations from the equilibrium point, it showed poor performance and sometimes instable behavior. Employing a nonlinear MPC Controller in real time was not possible due to the high computational effort involved. The RL controller exhibits by far the most versatile and robust control behavior. When operated in the simulation environment, it achieved a high control accuracy. When employed in the real system, however, it only shows average accuracy and a significantly greater performance loss compared to the simulation than the traditional controllers. With MATLAB, it is not yet possible to directly post-train the RL controller on the Raspberry Pi, which is an obstacle to the practical application of RL in a prototyping or teaching setting. Nevertheless, RL in general proves to be a flexible and powerful control method, which is well suited for complex or nonlinear systems where traditional controllers struggle.
To successfully develop and introduce concrete artificial intelligence (AI) solutions in operational practice, a comprehensive process model is being tested in the WIRKsam joint project. It is based on a methodical approach that integrates human, technical and organisational aspects and involves employees in the process. The chapter focuses on the procedure for identifying requirements for a work system that is implementing AI in problem-driven projects and for selecting appropriate AI methods. This means that the use case has already been narrowed down at the beginning of the project and must be completely defined in the following. Initially, the existing preliminary work is presented. Based on this, an overview of all procedural steps and methods is given. All methods are presented in detail and good practice approaches are shown. Finally, a reflection of the developed procedure based on the application in nine companies is given.
The emergence of automotive-grade LiDARs has given rise to new potential methods to develop novel advanced driver assistance systems (ADAS). However, accurate and reliable parking slot detection (PSD) remains a challenge, especially in the low-light conditions typical of indoor car parks. Existing camera-based approaches struggle with these conditions and require sensor fusion to determine parking slot occupancy. This paper proposes a parking slot detection (PSD) algorithm which utilizes the intensity of a LiDAR point cloud to detect the markings of perpendicular parking slots. LiDAR-based approaches offer robustness in low-light environments and can directly determine occupancy status using 3D information. The proposed PSD algorithm first segments the ground plane from the LiDAR point cloud and detects the main axis along the driving direction using a random sample consensus algorithm (RANSAC). The remaining ground point cloud is filtered by a dynamic Otsu’s threshold, and the markings of parking slots are detected in multiple windows along the driving direction separately. Hypotheses of parking slots are generated between the markings, which are cross-checked with a non-ground point cloud to determine the occupancy status. Test results showed that the proposed algorithm is robust in detecting perpendicular parking slots in well-marked car parks with high precision, low width error, and low variance. The proposed algorithm is designed in such a way that future adoption for parallel parking slots and combination with free-space-based detection approaches is possible. This solution addresses the limitations of camera-based systems and enhances PSD accuracy and reliability in challenging lighting conditions.
This paper serves as an introduction to the ECTS monitoring system and its potential applications in higher education. It also emphasizes the potential for ECTS monitoring to become a proactive system, supporting students by predicting academic success and identifying groups of potential dropouts for tailored support services. The use of the nearest neighbor analysis is suggested for improving data analysis and prediction accuracy.
The Inverted Rotary Pendulum: Facilitating Practical Teaching in Advanced Control Engineering
(2024)
This paper outlines a practical approach to teach control engineering principles, with an inverted rotary pendulum, serving as an illustrative example. It shows how the pendulum is embedded in an advanced course of control engineering. This approach is incorporated into a flipped-classroom concept, as well as classical teaching concepts, offering students practical experience in control engineering. In addition, the design of the pendulum is shown, using a Raspberry Pi as the target platform for Matlab Simulink. This pendulum can be used in the classroom to evaluate the controller design mentioned above. It is analysed if the use of the pendulum generates a deeper understanding of the learning contents.
In this field study we present an approach for the comprehensive and room-specific assessment of
parameters with the overall aim to realize energy-efficient provision of hygienically harmless and
thermally comfortable indoor environmental quality in naturally ventilated non-residential
buildings. The approach is based on (i) conformity assessment of room design parameters, (ii)
empirical determination of theoretically expected occupant-specific supply air flow rates and
corresponding air exchange rates, (iii) experimental determination of real occupant-specific
supply air flow rates and corresponding air exchange rates, (iv) measurement of indoor environmental
exposure conditions of T, RH, cCO2 , cPM2.5 and cTVOC, and (v) determination of real
energy demands for the prevailing ventilation scheme. Underlying assessment criteria comprise
the indoor environmental parameters of category II of EN 16798-1: Temperature T = 20 ◦C–24 ◦C,
and relative humidity RH = 25 %–60 % as well as the guide values of the German Federal
Environment Agency for cCO2 cPM2.5 and cTVOC of 1000 ppm, 15 μg m⁻³, and 1 mg m ⁻³,
respectively.
Investigation objects are six naturally ventilated classrooms of a German secondary school.
Major factors influencing indoor environmental quality in these classrooms are the specific room
volume per occupant and the window opening area. It is concluded that the rigorous implementation
of ventilation recommendations laid down by the German Federal Environment
Agency is ineffective with respect to anticipated indoor environmental parameters and inefficient
with respect to ventilation energy losses on the order of about 10 kWh m⁻² a ⁻¹ to 30 kWh m⁻²
a ⁻¹.