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Atmospheric plasma spraying (APS) is characterized by complex interactions between input, process and output variables. Process control relies heavily on human expert knowledge and experience. Process diagnostics can provide additional information to the operator and support cognitive processes in task execution. When using non-cascaded torch systems, significant plasma fluctuations occur, affecting the coating quality. High-frequency fluctuations can only be detected by suitable diagnostic systems and interpreted by experienced APS operators. In this study, the state of the plasma jet (area, fluctuation) is investigated depending on total plasma gas flow rate (50 vs. 65 l/min) and the H2 content of plasma gases (17, 20 and 23 vol. %) using high-speed camera pictures. To evaluate plasma fluctuation effects, particle temperature and velocity as well as resulting coating properties (thickness and porosity) are determined for two ceramic systems. The results show that fluctuations of the plasma jet have a significant effect on the particle state and coating quality. The use of a high-speed camera to evaluate the stability of the plasma jet is an attractive method that, when properly integrated, has the potential to provide the human operator with important information to allow rapid assessment of input parameters or the condition of the plasma torch.

When it comes to climate change, automated vehicles (AV) are often presented as a key factor to reducing emissions related with the transport sector. While studies promise emissions savings of up to 80%, it is often overlooked how AVs will be introduced and which transportation mode changes will arise from their implementation. Therefore, this online survey examined usage intentions regarding private and shared AV types, and underlying attitudes and mobility needs of 136 current users of different main modes of transport. Two main results counteract the general assumption of ecological sustainability benefits of AVs: First, current car drivers prefer private over shared AV types, even though notable sustainability gains can only be expected from shared AVs. Second, current users of more sustainable modes of transport (walking, bike, public transport) would replace theses modes by AVs for substantial shares of their trips, which represents a behavioural rebound effect, since AVs cannot be more sustainable than walking or biking. Group-specific mobility needs and knowledge gaps regarding the sustainability of different AV types are identified as reasons for these results and as starting points for deriving necessary measures accompanying the introduction of AVs into society through motivating ecologically sustainable transportation mode changes.

The phenomenon of the privacy paradox states that people are concerned about their data, but do not behave accordingly by avoiding disclosure of personal data, for instance, when using social media. Since findings about the influence of privacy concerns on privacy-related behavior are controversial, the present study investigates whether social norms or individual privacy concerns provide a better explanation of disclosure behavior. Therefore, a social media app dummy was developed and the social norm manipulated by altering the amount and the sensitivity of information provided in a fictitious app user profile. Regarding social norm factors, the results of the online experiment (N = 42) revealed that only the sensitivity of the data in the dummy profile demonstrated a medium effect on the amount of personal data given by the participants. Considering individual factors, the less concerned individuals were about their privacy, the higher were the amount and the degree of sensitivity of the personal information participants disclosed. In addition, participants differentiated between sensitive or non-sensitive information when disclosing their data. They disclosed sensitive information significantly less often, regardless of the dummy profile they saw. Altogether, the study results do not support the privacy paradox and demonstrate implications for media education.

ZusammenfassungDer Erfahrungsaustausch über gute Praxis ist eine Grundvoraussetzung für das Verständnis zwischen Mensch und Technik, welches dabei hilft, dem Menschen als strategischen Entscheider und Nutzer eine optimale Unterstützung zukommen zu lassen. Um der Frage nachzugehen, was gute Praxis im Bereich der Roboterprogrammierung auszeichnet, haben die Autoren eine qualitative und deskriptive Studie mit einem Vergleich zwischen n = 2 Versuchspersonen durchgeführt. Ein Experte und ein Novize bearbeiteten dieselbe Aufgabe: das Programmieren einer Roboterbahn zum Fräsen eines Werkstückes. Mittels Eye-Tracking Analysen und videounterstützter retrospektiver Think-Aloud Interviews wurden die Vorgehensweisen beider Probanden extrahiert und das Vorgehen anschließend formalisiert beschrieben. Zusätzlich ist ein qualitativer Vergleich zwischen den Endergebnissen gezogen worden, bei dem der Experte erwartungsgemäß besser als der Novize abschnitt. Auf Basis der Think-Aloud Protokolle wurden kognitive Prozesse identifiziert, die in diesem Kontext von besonderem Interesse sein könnten. Weiterhin wurden Augenbewegungscharakteristiken zur näheren Beschreibung einiger der kognitiven Prozesse dargelegt.Praktische Relevanz: Die vorliegende Arbeit zeigt ein methodisches Vorgehen zur formalisierten Beschreibung und Abbildung von menschlichem Expertenwissen beim Teach-in von Industrierobotern. Sie bildet die Basis für verschiedene zukünftige Projekte, z. B. das Erstellen von Richtlinien zum praktischen und effizienten Einlernen von Anfängern oder das Umsetzen von nutzerspezifischer Assistenz im Bereich des Teach-ins von Industrierobotern.

In this paper we introduce a new fuzzy system using adaptive fuzzy pattern classification (AFPC) for data-based online evolvement. The fuzzy pattern concept represents an efficient tool for handling uncertainty in multi-dimensional data streams and combines powerful performance, flexibility and meaningful interpretability within one consistent framework. We outline AFPC for non-linear, multi-dimensional transition processes, namely, for the identification of lane change intention in car driving. While lane changes are rare, they are highly safety-relevant transition processes, showing high fuzziness and large individual and inter-individual variations (e.g., in lane change duration). The method employs a combined knowledge- and data-based approach, and the underlying fuzzy potential membership function concept models expert knowledge, closely mirroring human cognition. The design of AFPC comprises (I) an initial training phase (off-line and supervised), which generates a meaningful start-classifier, (II) an online application phase, and finally (III) an evolvement phase (online and unsupervised). Here we consider parametric and structural adaptations and discuss prospects and future challenges. Furthermore, we present specific modeling results for such online data from a real driving study. Next-generation advanced driver assistance systems, as well as autonomously driven vehicles need to evolve, in terms of parameters and structure, based on online real-time data. AFPC presents an efficient tool for application in this area and others (e.g., medicine).

Modelle dienen der Speicherung von Wissen und als Basis fur Entscheidungen. Sie finden Anwendung in unterschiedlichsten Bereichen wie Technik, Medizin, Wirtschaft, Psychologie, Umwelt oder Verkehr. Gerade fur komplexe Zusammenhange sind Verfahren, die auf interpretierbaren Mustern beruhen, hoch flexibel und adaptiv. Die Theorie der Fuzzy Sets hat nun das Potenzial, gleitende Ubergange zwischen den Mustern zu beschreiben und damit realitatsnahe Modelle zu entwerfen. In dem Beitrag wird speziell die Fuzzy-Pattern-Klassifikation ausgefuhrt, die eine parametrische Zugehorigkeitsfunktion nutzt, mit der Muster auch in hochdimensionalen Merkmalsraumen beschrieben werden konnen. An zwei aktuellen, deutlich unterschiedlichen Anwendungen wird beispielhaft gezeigt, wie das gleiche Modellierungskonzept in humanwissenschaftlichen (psychologischen) und in technischen Bereichen einsetzbar ist. Konkret handelt es sich zum einen um den Einsatz linguistischer Antwortskalen in Fragebogenaktionen und zum anderen um die Zeitreihen-Prognose (konkret des fluktuierenden Energieertrags von Photovoltaikanlagen). Es ist Anliegen, hierbei zumindest exemplarisch den fundamentalen Charakter und damit auch die Transdisziplinaritat der Fuzzy Theorie zu zeigen.

The article describes a general two-step procedure for the numerical translation of vague linguistic terms (LTs). The suggested procedure consists of empirical and model components, including (1) participants' estimates of numerical values corresponding to verbal terms and (2) modeling of the empirical data using fuzzy membership functions (MFs), respectively. The procedure is outlined in two studies for data from N = 89 and N = 109 participants, who were asked to estimate numbers corresponding to 11 verbal frequency expressions (e.g., sometimes). Positions and shapes of the resulting MFs varied considerably in symmetry, vagueness, and overlap and are indicative of the different meanings of the vague frequency expressions. Words were not distributed equidistantly across the numerical scale. This has important implications for the many questionnaires that use verbal rating scales, which consist of frequency expressions and operate on the premise of equidistance. These results are discussed for an exemplar questionnaire (COPSOQ). Furthermore, the variation of the number of prompted LTs (5 vs. 11) showed no influence on the words' interpretations.

Abstract. In sequential diagnostic reasoning, observed pieces of evidence activate hypotheses in memory and are integrated to reach a final diagnosis. The order of evidence can influence diagnostic reasoning. This article examines the processing of ambiguous evidence underlying order effects if multiple hypotheses are activated. In five experiments with a quasi-medical scenario, participants dealt with symptom sequences supporting multiple diagnoses. The symptom order, the response mode (end-of-sequence, step-by-step), and the consistency of evidence were manipulated. A primacy order effect occurred with both response modes suggesting that ambiguous pieces of evidence were distorted toward the hypothesis that strongly corresponded with the first piece. The primacy effect was partially counteracted by stepwise belief ratings, which strengthened the weight of recent evidence and promoted switching to an alternative diagnosis. We conclude that once hypotheses are generated, the interplay of coherence-oriented information distortion and memory-dependent analytic processes propagates into distinct order effects in diagnoses.

The paper describes a general two-step procedure for the numerical translation of linguistic terms using parametric fuzzy potential membership functions. In an empirical study 121 participants estimated numerical values that correspond to 13 verbal probability expressions. Among the estimates are the most typical numerical equivalent and the minimal and maximal values that just correspond to the given linguistic terms. These values serve as foundation for the proposed fuzzy approach. Positions and shapes of the resulting membership functions suggest that the verbal probability expressions are not distributed equidistantly along the probability scale and vary considerably in symmetry, vagueness and overlap. Therefore we recommend the proposed empirical procedure and fuzzy approach for future investigations and applications in the area of decision support.