Refine
Year of publication
Document Type
- Article (625)
- Conference Proceeding (296)
- Book (113)
- Part of a Book (61)
- Patent (15)
- Report (9)
- Other (8)
- Contribution to a Periodical (6)
- Course Material (6)
- Doctoral Thesis (6)
- Bachelor Thesis (1)
- Video (1)
- Poster (1)
- Review (1)
- Talk (1)
Language
- English (693)
- German (456)
- Multiple languages (1)
Keywords
- Multimediamarkt (7)
- Enterprise Architecture (5)
- MINLP (5)
- Engineering optimization (4)
- Gamification (4)
- Serious Game (4)
- Auslenkung (3)
- Digitale Transformation (3)
- Digitalisierung (3)
- Education (3)
- Javasimulation (3)
- Literaturanalyse (3)
- Optimization (3)
- Powertrain (3)
- Referenzmodellierung (3)
- Robotic Process Automation (3)
- Technical Operations Research (3)
- Telecommunication (3)
- Amplitude (2)
- Autonomous mobile robots (2)
Institute
- Fachbereich Elektrotechnik und Informationstechnik (1150) (remove)
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.
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.
This paper presents an approach for reducing the cognitive load for humans working in quality control (QC) for production processes that adhere to the 6σ -methodology. While 100% QC requires every part to be inspected, this task can be reduced when a human-in-the-loop QC process gets supported by an anomaly detection system that only presents those parts for manual inspection that have a significant likelihood of being defective. This approach shows good results when applied to image-based QC for metal textile products.