Deep neural networks as surrogate models for time-efficient manufacturing process optimisation

Manufacturing process optimisation usually amounts to searching optima in high-dimensional parameter spaces. In industrial practice, this search is most often directed by human-subjective expert judgment and trial-and-error experiments. In contrast, high-fidelity simulation models in combination with general-purpose optimisation algorithms, e.g. finite element models and evolutionary algorithms, enable a methodological, virtual process exploration and optimisation. However, reliable process models generally entail significant computation times, which often renders classical, iterative optimisation impracticable. Thus, efficiency is a key factor in optimisation. One option to increase efficiency is surrogate-based optimisation (SBO): SBO seeks to reduce the overall computational load by constructing a numerically inexpensive, data-driven approximation („surrogate“) of the expensive simulation. Traditionally, classical regression techniques are applied for surrogate construction. However, they typically predict a predefined, scalar performance metric only, which limits the amount of usable information gained from simulations. The advent of machine learning (ML) techniques introduces additional options for surrogates: in this work, a deep neural network (DNN) is trained to predict the full strain field instead of a single scalar during textile forming („draping“). Results reveal an improved predictive accuracy as more process-relevant information from the supplied simulations can be extracted. Application of the DNN in an SBO-framework for blank holder optimisation shows improved convergence compared to classical evolutionary algorithms. Thus, DNNs are a promising option for future surrogates in SBO

Establishing semantic research graphs in humanities\u27 research practice

This paper presents approaches and challenges to developing research software for qualitative humanities and social sciences research. While these methods so far lack a digital enhancement, we realized a range of research environments based on semantic graph technologies in research practice. In order to understand the researchers\u27 needs and take the existing research practice seriously, a participatory and agile design approach was used by which several researchers were involved in realizing concrete research projects. A user-centered agile approach with fast prototyping was included. To materialize the needed interaction at an early stage of the research life cycle, this was accompanied by trainings, workshops, and joint paper publications in the different research communities. (DIPF/Orig.

The theater-system technique: agile designing and testing of system behavior and interaction, applied to highly automated vehicles

In this paper, the theater-system technique, a method for agile designing and testing of system behavior and interaction concepts is described. The technique is based on the Wizard-of-Oz approach, originally used for emulating automated speech recognition, and is extended towards an interactive, user-centered design technique. The paper describes the design process using the theater-system technique, the technical build-up of the theater-system, and an application of the technique: the design of a haptic-multimodal interaction strategy for highly automated vehicles. The use of the theater-system in the design process is manifold: It is used for the concrete design work of the design team, for the assessment of user expectations as well as for early usability assessments, extending the principles of user-centered design towards a dynamically balanced design

ICT Infrastructure for Cooperative, Connected and Automated Transport in Transition Areas

One of the challenges of automated road transport is to manage the coexistence of conventional and highly automated vehicles, in order to ensure an uninterrupted level of safety and efficiency. Vehicles driving at a higher automation level may have to change to a lower level of automation in a certain area under certain circumstances and certain (e.g. road and weather) conditions. The paper targets the transition phases between different levels of automation. It will review related research, introduce a concept to investigate automation level changes, present some recent research results, i.e. assessing key performance indicators for both analysing driver behaviour and traffic management in light of autonomous vehicles, an initial simulation architecture, and address further research topics on investigation of the traffic management in such areas (called "Transition Areas") when the automation level changes, and development of traffic management procedures and protocols to enable smooth coexistence of automated, cooperative, connected vehicles and conventional vehicles, especially in an urban environment

The Proper Formulation of the Minimalist Theory of Truth

Minimalism about truth is one of the main contenders for our best theory of truth, but minimalists face the charge of being unable to properly state their theory. Donald Davidson incisively pointed out that minimalists must generalize over occurrences of the same expression placed in two different contexts, which is futile. In order to meet the challenge, Paul Horwich argues that one can nevertheless characterize the axioms of the minimalist theory. Sten Lindström and Tim Button have independently argued that Horwich’s attempt to formulate minimalism remains unsuccessful. We show how to properly state Horwich’s axioms by appealing to propositional functions that are given by definite descriptions. Both Lindström and Button discuss proposals similar to ours and conclude that they are unsuccessful. Our new suggestion avoids these objections

Educational Open Government Data in Germany: The Landscape, Status, and Quality

This paper examines the educational datasets of Open Government Portals (OGPs) in Germany. While OGPs has become an important player in making public data available, the quality and coverage are increasingly problematized. This study analyzes the quality of 28 OGPs, 18 on the city-level, seven on the state-level (the city-states of Berlin, Hamburg, and Bremen were counted as states), and three at the national level. The focus of this study is on identifying the gap in open educational data. The results show that the total number of datasets and the educational data varies very much between the portals. While the portals contain 94% ‘open’ datasets, most of the portals do not provide comprehensive usage metrics like the data download, evidence of usage in research or applications. Finally, the paper highlights the main shortcomings of the existing open data portals regarding the quality of datasets and the lack of findability and granularity

A Bayesian Approach with Prior Mixed Strategy Nash Equilibrium for Vehicle Intention Prediction

The state-of-the-art technology in the field of vehicle automation will lead to a mixed traffic environment in the coming years where Connected and Automated Vehicles (CAVs) have to interact with Human-Driven ones (HVs). In this context, it is necessary to have intention prediction models with the capability of forecasting how the traffic scenario is going to evolve with respect to the physical state of vehicles, the possible maneuvers and the interactions between the traffic participants within the seconds to come. This article presents a Bayesian approach for vehicle intention forecasting, proposing as prior estimate a game-theoretic framework in the form of a Mixed Strategy Nash Equilibrium (MSNE) to model the reciprocal influence between traffic participants. The likelihood is then computed based on the Kullback-Leibler divergence. The game is modeled as a static nonzero-sum polymatrix game with individual preferences, a well known strategic game. Finding the MSNE for these games is in the PPAD \ PLS complexity class, with polynomial-time tractability. The approach shows good results in simulation in the long term horizon (10s), with its computational complexity allowing for online applications

Managed Automated Driving (MAD) - a Concept for Empowering Road Infrastructure

Current automated vehicles, even when appended with C-ITS support, require a vast number of sensors of various types per vehicle to empower them to cope with the variety of possible situations. However, most of these sensors are not required on large parts of the route while in addition, the horizon of vehiclebased sensors is very limited. On the other hand, roadside infrastructure can be placed fully matching specific local requirements and therefore ensuring optimal sensor performance, while communication can provide basically unlimited sensor ranges. Replacing vehicle-based sensors by infrastructuremounted ones can therefore be a solution. Using this approach on dedicated routes (e.g. for public or freight transport) with fleet vehicles can be a starting point to make automated driving safer and economically beneficial. The German national project "MAD Urban" is investigating means of additional roadside support, including localization, object detection, and even vehicle control by infrastructure