Principal Flow Patterns across renewable electricity networks

Using Principal Component Analysis (PCA), the nodal injection and line flow patterns in a network model of a future highly renewable European electricity system are investigated. It is shown that the number of principal components needed to describe 95$\%$ of the nodal power injection variance first increases with the spatial resolution of the system representation. The number of relevant components then saturates at around 76 components for network sizes larger than 512 nodes, which can be related to the correlation length of wind patterns over Europe. Remarkably, the application of PCA to the transmission line power flow statistics shows that irrespective of the spatial scale of the system representation a very low number of only 8 principal flow patterns is sufficient to capture 95$\%$ of the corresponding spatio-temporal variance. This result can be theoretically explained by a particular alignment of some principal injection patterns with topological patterns inherent to the network structure of the European transmission system

Band gap engineering by Bi intercalation of graphene on Ir(111)

We report on the structural and electronic properties of a single bismuth layer intercalated underneath a graphene layer grown on an Ir(111) single crystal. Scanning tunneling microscopy (STM) reveals a hexagonal surface structure and a dislocation network upon Bi intercalation, which we attribute to a $\sqrt{3}\times\sqrt{3}R30{\deg}$ Bi structure on the underlying Ir(111) surface. Ab-initio calculations show that this Bi structure is the most energetically favorable, and also illustrate that STM measurements are most sensitive to C atoms in close proximity to intercalated Bi atoms. Additionally, Bi intercalation induces a band gap ($E_g=0.42\,$eV) at the Dirac point of graphene and an overall n-doping ($\sim 0.39\,$eV), as seen in angular-resolved photoemission spectroscopy. We attribute the emergence of the band gap to the dislocation network which forms favorably along certain parts of the moir\'e structure induced by the graphene/Ir(111) interface.Comment: 5 figure

Enabling non-engineers to use engineering tools: introducing product development to pupils using knowledge-integrating systems

Many engineering tasks are supported by tools based on innovative technologies. Powerful tools for computer aided design, simulations or programming permit a wide range of possibilities for engineers in solving complex problems. However, using these tools commonly requires extensive training or specific skills. Specialized systems that enable tool and technology usage could support novices in solving engineering tasks using embedded knowledge, lowering the hurdle of expertise required for operation. In the presented case study, knowledge-integrating systems inspired by knowledge-based engineering were developed to allow pupils to solve an engineering challenge without existing skills or prior training. To provide a realistic application context, a teaching module was developed, introducing high school students to product engineering in the form of a conceive-design-implement-operate experience with the learning goal to engage them in the STEM field. Solving the included engineering challenge required the creation, test and iteration of designs for laser cut and additive manufacturing, and code processing sensor signals for motor actuation. To evaluate the knowledge-integrating systems in their use qualitatively, a trial run was conducted. Participants were enabled to fulfil basic product engineering tasks and expressed engagement in product development and overall satisfaction. The module’s key element is an educational exoskeleton that can be controlled by electromyography signals. It is modified to eventually support a fictional character suffering from monoplegia. The module was realized accompanying the CYBATHLON, a championship for people with physical disabilities in solving everyday tasks assisted by state-of-the-art technical systems

Batch Normalization Provably Avoids Rank Collapse for Randomly Initialised Deep Networks

Randomly initialized neural networks are known to become harder to train with increasing depth, unless architectural enhancements like residual connections and batch normalization are used. We here investigate this phenomenon by revisiting the connection between random initialization in deep networks and spectral instabilities in products of random matrices. Given the rich literature on random matrices, it is not surprising to find that the rank of the intermediate representations in unnormalized networks collapses quickly with depth. In this work we highlight the fact that batch normalization is an effective strategy to avoid rank collapse for both linear and ReLU networks. Leveraging tools from Markov chain theory, we derive a meaningful lower rank bound in deep linear networks. Empirically, we also demonstrate that this rank robustness generalizes to ReLU nets. Finally, we conduct an extensive set of experiments on real-world data sets, which confirm that rank stability is indeed a crucial condition for training modern-day deep neural architectures

Open Source Geospatial Applications to support River Basin Management in Kharaa River Basin, Mongolia

An open access geoportal, developed using open source technologies, delivers a comprehensive overview of all geodata available for the Kharaa River Basin in Mongolia. The Water Quality Monitoring Database is made available in PostgreSQL/PostGIS format and embedded in the geoportal. Web maps in the Geoportal can be linked to monitoring data using interactive queries to the Water Quality Database enabling water managers to create thematic maps on demand for the development of a comprehensive river basin management plan for the Kharaa River Basin