Effective strategies for enumeration games

We study the existence of effective winning strategies in certain infinite games, so called enumeration games. Originally, these were introduced by Lachlan (1970) in his study of the lattice of recursively enumerable sets. We argue that they provide a general and interesting framework for computable games and may also be well suited for modelling reactive systems. Our results are obtained by reductions of enumeration games to regular games. For the latter effective winning strategies exist by a classical result of Buechi and Landweber. This provides more perspicuous proofs for several of Lachlan\u27s results as well as a key for new results. It also shows a way of how strategies for regular games can be scaled up such that they apply to much more general games

Prediction of tool forces in manual grinding using consumer-grade sensors and machine learning

Tool forces are a decisive parameter for manual grinding with hand-held power tools, which can be used to determine the productivity, quality of the work result, vibration exposition, and tool lifetime. One approach to tool force determination is the prediction of tool forces via measured operating parameters of a hand-held power tool. The problem is that the accuracy of tool force prediction with consumer-grade sensors remains unclear in manual grinding. Therefore, the accuracy of tool force prediction using Gaussian process regression is examined in a study for two hand-held angle grinders in four different applications in three directions using measurement data from an inertial measurement unit, a current sensor, and a voltage sensor. The prediction of the grinding normal force (rMAE = 11.44% and r = 0.84) and the grinding tangential force (rMAE = 18.21% and r = 0.82) for three tested applications, as well as the radial force for the application cutting with a cut-off wheel (rMAE = 19.67% and r = 0.80) is shown to be feasible. The prediction of the guiding force (rMAE = 87.02% and r = 0.37) for three tested applications is only possible to a limited extent. This study supports data acquisition and evaluation of hand-held power tools using consumer-grade sensors, such as an inertial measurement unit, in real-world applications, resulting in new potentials for product use and product development

Design of sensor integrating gears: methodical development, integration and verification of an in-Situ MEMS sensor system

State of the art vibration-based condition monitoring at gearbox housings faces uncertainties in the interpretation of measurement data due to signal transformations and noise. The state of research shows that direct measurements at the source of vibrations with integrated sensors provide higher quality data. Capacitive MEMS sensors seem predestined for integration, but there is limited research covering compactly integrated MEMS sensor systems for condition monitoring by vibration measurement. In this contribution an integrated MEMS sensor system is designed methodically based on VDI 2206. A sensor system is selected based on requirements extracted of previous contributions and verified on a rotational shaker test rig. Afterwards it is integrated on a gear wheel in a gear test bench. Several verification measurements using different principles and locations are performed to verify the measurands. Results show that the gear mesh vibrations including the sidebands can be measured with the integrated sensors which provide superior signal-noise-ratios compared to other locations. This proofs that the sensor integrating gear system is principally able to perform high quality condition monitoring

Sensor-integrating gears: wear detection by in-situ MEMS acceleration sensors [Sensorintegrierende Zahnräder: Verschleißdetektion durch In-situ MEMS Beschleunigungssensoren]

Gear tooth wear is a common phenomenon leading to malfunctions in machines. To detect wear and faults, gear condition monitoring by vibration is established. The problem is that the measurement data quality for detection of wear by vibration is not good enough with currently established measurement methods, caused by long signal paths of the commonly used housing mounted sensors. In-situ sensors directly at the gear achieve better data quality, but are not yet proved in wear detection. Further it is unknown what analysis methods are suited for in-situ sensor data. Existing gear condition metrics are mainly focused on localized gear tooth faults, and do not estimate wear related values. This contribution aims to improve wear detection by investigating in-situ sensors and advance gear condition metrics. Using a gear test rig to conduct an end of life test, the wear detection ability of an in-situ sensor system and reference sensors on the bearing block are compared through standard gear condition metrics. Furthermore, a machine-learned regression model is developed that maps multiple features related to gear dynamics to the gear mass loss. The standard gear metrics used on the in-situ sensor data are able to detect wear, but not significantly better compared to the other sensors. The regression model is able to estimate the actual wear with a high accuracy. Providing a wear related output improves the wear detection by better interpretability

Structural measures for games and process control in the branch learning model

Process control problems can be modeled as closed recursive games. Learning strategies for such games is equivalent to the concept of learning infinite recursive branches for recursive trees. We use this branch learning model to measure the difficulty of learning and synthesizing process controllers. We also measure the difference between several process learning criteria, and their difference to controller synthesis. As measure we use the information content (i.e. the Turing degree) of the oracle which a machine need to get the desired power. The investigated learning criteria are finite, EX-, BC-, Weak BC- and online learning. Finite, EX- and BC-style learning are well known from inductive inference, while weak BC- and online learning came up with the new notion of branch (i.e. process) learning. For all considered criteria - including synthesis - we also solve the questions of their trivial degrees, their omniscient degrees and with some restrictions their inference degrees. While most of the results about finite, EX- and BC-style branch learning can be derived from inductive inference, new techniques had to be developed for online learning, weak BC-style learning and synthesis, and for the comparisons of all process learning criteria with the power of controller synthesis

Thermosensitive Cu2O-PNIPAM core-shell nanoreactors with tunable photocatalytic activity

We report a facile and novel method for the fabrication of Cu2O@PNIPAM core-shell nanoreactors using Cu2O nanocubes as the core. The PNIPAM shell not only effectively protects the Cu2O nanocubes from oxidation, but also improves the colloidal stability of the system. The Cu2O@PNIPAM core-shell microgels can work efficiently as photocatalyst for the decomposition of methyl orange under visible light. A significant enhancement in the catalytic activity has been observed for the core-shell microgels compared with the pure Cu2O nanocubes. Most importantly, the photocatalytic activity of the Cu2O nanocubes can be further tuned by the thermosensitive PNIPAM shell, as rationalized by our recent theory.Comment: 8 pages, 6 figures (Supporting Information included: 11 pages, 10 figures

Evaluation of Transformer Architectures for Electrical Load Time-Series Forecasting

Accurate forecasts of the electrical load are needed to stabilize the electrical grid and maximize the use of renewable energies. Many good forecasting methods exist, including neural networks, and we compare them to the recently developed Transformers, which are the state-of-the-art machine learning technique for many sequence-related tasks. We apply different types of Transformers, namely the Time-Series Transformer, the Convolutional Self-Attention Transformer and the Informer, to electrical load data from Baden-Württemberg. Our results show that the Transformes give up to 11% better forecasts than multi-layer perceptrons for long prediction horizons. Furthermore, we analyze the Transformers’ attention scores to get insights into the model

Klimaschutzpolitik - ist das Emissionshandelssystem ein effizientes Mittel zur Emissionsverringerung?

Der Emissionshandel könnte als flexibles Instrument zum Erreichen der Reduktionsziele beitragen. Allerdings sollte er, nach Meinung von Dr. Angelika Zahmt und Matthias Seiche, BUND, mit der ökologischen Steuerreform verknüpft werden. Für Dr. Friedemann Müller, Stiftung Wissenschaft und Politik, kann der Handel nur in Verbindung mit »einer gleichen Verteilung von Emissionsrechten pro Kopf« ein Ansatz zur Lösung des Klimaproblems sein. Auch nach Ansicht von Dr. Hermann E. Ott und Thomas Langrock, Wuppertal Institut, sprechen gute Gründe für einen internationalen Emissionshandel. Für Prof. Dr. Wolfgang Ströbele, Universität Münster, ist zur Lösung des Emissionsproblems der konkrete EU-Richtlinienvorschlag »wenig nützlich«.Umweltzertifikat; Umweltbelastung; Klimaschutz; EU-Recht