In this article, two data-driven modeling approaches are investigated, which allow an explicit modeling of uncertainty. For this purpose, parametric Takagi-Sugeno multi-models with bounded-error parameter estimation and nonparametric Gaussian process regression are applied and compared. These models can for instance be used for robust model-based control design. As an application, the prediction of residual stresses during hard turning depending on the machining parameters and the initial hardness is considered.

Continuous Kernels have been a recent development in convolutional neural networks. Such kernels are used to process data sampled at different resolutions as well as irregularly and inconsistently sampled data. Convolutional neural networks have the property of translational invariance (e.g., features are detected regardless of their position in the measurement domain), which is unsuitable if the position of detected features is relevant for the prediction task. However, the capabilities of continuous kernels to ...

Carbon nanotubes possess many interesting properties, which make them a promising material for a variety of applications. In this paper, we present a unified framework for the simulation of the mechanical behavior of carbon nanotubes. It allows the creation, simulation and visualization of these structures, extending previous work by the research group “MISMO” at TU Darmstadt. In particular, we develop and integrate a new matrix-free iterative solving procedure, employing the conjugate gradient method, that drastically ...

Technology-mediated learning (TML) is a major trend in education, since it allows to integrate the strengths of traditional- and IT-based learning activities. However, TML providers still struggle in identifying areas for improvement in their TML offerings. One reason for their struggles is inconsistencies in the literature regarding drivers of TML performance. Prior research suggests that these inconsistencies in TML literature might stem from neglecting the importance of considering the process perspective in ...

What if artificial intelligence (AI) machines became teammates rather than tools? This paper reports on an international initiative by 65 collaboration scientists to develop a research agenda for exploring the potential risks and benefits of machines as teammates (MaT). They generated 819 research questions. A subteam of 12 converged them to a research agenda comprising three design areas – Machine artifact, Collaboration, and Institution – and 17 dualities – significant effects with the potential for benefit or harm. ...

Trust is the enabler of social interaction. Although the origins of research on trust traditionally lie outside the Information Systems (IS) domain, the importance of trust for IS research rapidly grew in the late 1990s, and it is still growing with the increasing ubiquity and advancement of technology in organizations, virtual teams, online markets, and user-technology interactions. Theoretically, the central role of trust is tied to the growing social change that Information and Communication Technology (ICT) has ...

IT support in the learning process constitutes a key factor for the success of innovative teaching/learning scenarios. To ensure learning success in innovative teaching/learning scenarios, learners need to faithfully apply learning management systems (LMS). However, we lack theoretical insights into which factors affect whether they do so. To help solve this issue, we first used adaptive structuration theory to identify antecedents and consequences regarding faithful LMS appropriation and embed them into a theoretical ...

We present a new algorithm called TITANIC for computing concept lattices. It is based on data mining techniques for computing frequent itemsets. The algorithm is experimentally evaluated and compared with B. Ganter's Next-Closure algorithm.