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Highly competitive markets paired with tremendous production volumes demand particularly cost efficient products. The usage of common parts and modules across product families can potentially reduce production costs. Yet, increasing commonality typically results in overdesign of individual products. Multi domain virtual prototyping enables designers to evaluate costs and technical feasibility of different single product designs at reasonable computational effort in early design phases. However, savings by platform commonality are hard to quantify and require detailed knowledge of e.g. the production process and the supply chain. Therefore, we present and evaluate a multi-objective metamodel-based optimization algorithm which enables designers to explore the trade-off between high commonality and cost optimal design of single products.
Against the background of growing data in everyday life, data processing tools become more powerful to deal with the increasing complexity in building design. The architectural planning process is offered a variety of new instruments to design, plan and communicate planning decisions. Ideally the access to information serves to secure and document the quality of the building and in the worst case, the increased data absorbs time by collection and processing without any benefit for the building and its user. Process models can illustrate the impact of information on the design- and planning process so that architect and planner can steer the process. This paper provides historic and contemporary models to visualize the architectural planning process and introduces means to describe today’s situation consisting of stakeholders, events and instruments. It explains conceptions during Renaissance in contrast to models used in the second half of the 20th century. Contemporary models are discussed regarding their value against the background of increasing computation in the building process.
Enzyme und Biosensorik
(2018)
Enzymbasierte Biosensoren finden seit mehr als fünf Jahrzehnten einen prosperierenden Wachstumsmarkt und werden zunehmend auch in biotechnologischen Prozessen eingesetzt. In diesem Kapitel werden, ausgehend vom Sensorbegriff und typischen Kenngrößen für Biosensoren (Abschn. 18.1), elektrochemische Enzym-Biosensoren vorgestellt und deren typischen Einsatzgebiete diskutiert (Abschn. 18.2). Ein Blick über den „Tellerrand“ hinaus zeigt alternative Transduktorprinzipien (Abschn. 18.3) und führt abschließend in aktuelle Forschungstrends ein (Abschn. 18.4).
Sleep scoring is a necessary and time-consuming task in sleep studies. In animal models (such as mice) or in humans, automating this tedious process promises to facilitate long-term studies and to promote sleep biology as a data-driven f ield. We introduce a deep neural network model that is able to predict different states of consciousness (Wake, Non-REM, REM) in mice from EEG and EMG recordings with excellent scoring results for out-of-sample data. Predictions are made on epochs of 4 seconds length, and epochs are classified as artifactfree or not. The model architecture draws on recent advances in deep learning and in convolutional neural networks research. In contrast to previous approaches towards automated sleep scoring, our model does not rely on manually defined features of the data but learns predictive features automatically. We expect deep learning models like ours to become widely applied in different fields, automating many repetitive cognitive tasks that were previously difficult to tackle.
This paper presents NLP Lean Programming
framework (NLPf), a new framework
for creating custom natural language processing
(NLP) models and pipelines by utilizing
common software development build systems.
This approach allows developers to train and
integrate domain-specific NLP pipelines into
their applications seamlessly. Additionally,
NLPf provides an annotation tool which improves
the annotation process significantly by
providing a well-designed GUI and sophisticated
way of using input devices. Due to
NLPf’s properties developers and domain experts
are able to build domain-specific NLP
applications more efficiently. NLPf is Opensource
software and available at https://
gitlab.com/schrieveslaach/NLPf.