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Increasing global awareness of the benefits of electromobility has brought about the need for new concepts in terms of the acoustic design of future vehicle generations. This includes both the creative design process and the development of suitable methods for the evaluation of target sounds by trial participants. The main difference between e-car sound surveys and those carried out on familiar sound categories is the potential consumer’s lack of experience with electric vehicles. Thus the consumer has no, or very unspecific, expectations in this regard. Several studies have consequently shown that many subjects have to construct their personal frame of reference for evaluation within the listening experiment. However, this is possibly at odds with experience-based expectations relating to the sounds of conventional combustion engines. The result is a conflict of objectives between the traditional and the modern, familiarity and strangeness and not least between driving freedom and ecological awareness. In this context, the authenticity of the sound and the subjective interpretability of the sound information also appear as moderator variables. Moreover, associations with other vehicle categories, for example streetcars, also influence the perceived sound quality. In the course of this contribution, these factors will be expounded and their influence on the evaluation of interior noise discussed.
Undergraduate students in engineering often have little exposure to the world of scientific publishing and the culture of sharing research work. While the beneficial exchange between research and teaching is well promoted, while the benefits of conference participation for students have already been surveyed and while courses on scientific writing are readily available, the concept of mini-conferences as part of the curriculum combines all three aspects into one. Therefore, the course "Engineering Conferences" was developed and installed as a mandatory part of a master program for engineering students. The idea is to go beyond simply teaching the standards of academic writing and skills for using scientific publications. By using a learner-centered approach, the students are engaged in typical activities around an active attendance of a real conference. Based on their bachelor thesis, they write a paper complying with common academic standards, submit the paper and review submissions of their fellow students. They also produce a poster and defend their work in a poster session held publicly on campus. This contribution is based on the experience from the first four terms teaching the course. It explains the didactic rationale behind the concept and individual teaching modules, it comprises the collection of useful resources for teaching and organizing scientific publishing and it includes the consequences drawn from course evaluation results.
The importance of high data quality is increasing with the growing impact and distribution of ML systems and big data. Also the planned AI Act from the European commission defines challenging legal requirements for data quality especially for the market introduction of safety relevant ML systems. In this paper we introduce a novel approach that supports the data quality assurance process of multiple data quality aspects. This approach enables the verification of quantitative data quality requirements. The concept and benefits are introduced and explained on small example data sets. How the method is applied is demonstrated on the well known MNIST data set based an handwritten digits.
The importance of high data quality is increasing with the growing impact and distribution of ML systems and big data. Also, the planned AI Act from the European commission defines challenging legal requirements for data quality especially for the market introduction of safety relevant ML systems. In this paper, we introduce a novel approach that supports the data quality assurance process of multiple data quality aspects. This approach enables the verification of quantitative data quality requirements. The concept and benefits are introduced and explained on small example data sets. How the method is applied is demonstrated on the well-known MNIST data set based an handwritten digits.