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CrashNet
(2021)
Destructive car crash tests are an elaborate, time-consuming, and expensive necessity of the automotive development process. Today, finite element method (FEM) simulations are used to reduce costs by simulating car crashes computationally. We propose CrashNet, an encoder-decoder deep neural network architecture that reduces costs further and models specific outcomes of car crashes very accurately. We achieve this by formulating car crash events as time series prediction enriched with a set of scalar features. Traditional sequence-to-sequence models are usually composed of convolutional neural network (CNN) and CNN transpose layers. We propose to concatenate those with an MLP capable of learning how to inject the given scalars into the output time series. In addition, we replace the CNN transpose with 2D CNN transpose layers in order to force the model to process the hidden state of the set of scalars as one time series. The proposed CrashNet model can be trained efficiently and is able to process scalars and time series as input in order to infer the results of crash tests. CrashNet produces results faster and at a lower cost compared to destructive tests and FEM simulations. Moreover, it represents a novel approach in the car safety management domain.
The business problem of having inefficient processes, imprecise process analyses, and simulations as well as non-transparent artificial neuronal network models can be overcome by an easy-to-use modeling concept. With the aim of developing a flexible and efficient approach to modeling, simulating, and optimizing processes, this paper proposes a flexible Concept of Neuronal Modeling (CoNM). The modeling concept, which is described by the modeling language designed and its mathematical formulation and is connected to a technical substantiation, is based on a collection of novel sub-artifacts. As these have been implemented as a computational model, the set of CoNM tools carries out novel kinds of Neuronal Process Modeling (NPM), Neuronal Process Simulations (NPS), and Neuronal Process Optimizations (NPO). The efficacy of the designed artifacts was demonstrated rigorously by means of six experiments and a simulator of real industrial production processes.
The design of qualitative, excellent teaching requires collaboration between teachers and learners. For this purpose, face-to-face teaching benefits from a long-standing tradition, while digital teaching is comparatively still at the beginning of its dissemination. A major developmental step toward the digitization of teaching was achieved in the context of university teaching during the Covid 19 pandemic in spring 2020, when face-to-face teaching was interrupted for months. During this time, important insights into the opportunities and limitations of digital teaching were gained. This paper presents selected results of a study conducted at four German universities and with 875 responses in spring 2020. The study uncovers opportunities and limitations of digital teaching from the students’ perspective and against the background of their experience in the completely digital semester. The results are used as a basis for deriving design guidelines for digital teaching and learning offerings. Based on a model for analyzing the design of teaching and learning formats, these indications are structured according to the elements learners, teachers, teaching content, environment and teaching style.