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In this study, we compare different machine learning approaches applied to acoustic resonance recognition of coins. Euro-cents and Euro-coins were classified by the sound emerging when throwing the coins onto a hard surface.The used dataset is a representative example of a small data which was collected in carefully prepared experiments.Due to the small number of coin specimens and the count of the collected observations, it was interesting to see whether deep learning methods can achieve similarly or maybe even better classification performances compared with more traditional methods.The results of the multi-class prediction of coin denominations are presented and compared in terms of balanced accuracy and Matthews Correlation Coefficient metrics. The feature analysis methods combined with the employed classifiers achieved acceptable results, despite the relatively small dataset.
Das Fachgebiet der Kognitiven Technischen Systeme zeichnet sich durch einen hohen Grad an Interdisziplinarität (z. B. Kenntnisse auf den Gebieten Biologie, Psychologie, Informatik und den Ingenieurwissenschaften) aus. Nach wie vor besteht Bedarf an einer methodischen Darstellung des Fachgebietes, bei der die theoretische Durchdringung von Zusammenhängen zwischen den verschiedenen Wissensgebieten zusätzlich durch Anschaulichkeit unterstützt wird. Die vorliegende Arbeit stellt einen Fortschrittsbericht zur Realisierung eines Forschungs- und Experimentiersystems dar, mit dem wir dieses Anliegen unterstützen und über das wir erstmals konzeptionell in [1] berichtet haben. In diesem Beitrag folgen wir einem integrativen Ansatz zur Entwicklung einer kognitiven Architektur, mit der unter Verwendung repräsentationaler Datenstrukturen adaptives Verhalten auf verschiedenen Zeitskalen sowie zwei wichtige Verhaltensprogramme für das Problemlösen (Objektfindung, Exploration) auf der gemeinsamen Grundlage von Markov-Entscheidungsprozessen umgesetzt werden. Mit einem Kurzbericht zum Entwicklungsstand der physikalischen Experimentierumgebung und einer Zusammenfassung der bislang erreichten Ergebnisse beschließen wir den diesjährigen Beitrag.
Diamond-like carbon (DLC) coatings are often utilized to change the base properties of raw materials, e. g. to gain robust or chemically inert surfaces. Here the deposition of hard DLC layers on soft polymers is tested. The common industrial polymers polyethylene (PE) and polyoxymethylene (POM) serve as model substrates in the present work. In order to examine the effect of the carbonaceous layers on PE and POM thin diamond-like carbon films were realized via PECVD. The topography of the different coatings was detected by AFM and SEM uncovering the different influence of the substrate material, e. g. interlayer formation for PE. The composition of the surface structures were analyzed in more detail by NEXAFS, showing the hybridization of carbon atoms in chemical different surroundings from the pure polymer to the carbon coating. The comparison of carbon coated PE- and POM-composites enables valuable insights into the interaction between soft substrates and hard coatings. For the chosen settings in PECVD, POM showed incomplete coating, while on PE a very stable and robust composite has formed.
The coating of materials with diamond-like carbon (DLC) is a very common way to change and improve their basic characteristics. Although DLC is used on several substrates, the chemical and physical properties throughout the coating process on plastics are yet sparsely investigated. Two types of protective
coatings one sp3-enriched (robust, r-type) and one with more sp2-centers (flexible, f-type) have been realized on polyethylene by PECVD deposition. SEM and AFM analysis of coated samples of DLC types revealed diverse surface topographies on different scales and images appeared even differently smoothed by the carbonaceous deposits. Grains of both DLC types are platelet-shaped and nearly double in size for the robust type indicating fundamental differences in the epitaxial DLC growth. NEXAFS spectroscopy showed significant details of carbon centers in chemically different neighborhood displaying a characteristic fingerprint behavior. Comparison of deposition models revealed a mechanism of interlayer formation which is discussed in detail. Interlayer formation is clearly the appropriate explanation of the process for the current carbon deposition between these two unequal materials. An improved understanding of hard DLC and soft polyethylene assembly is given in the presented work.