Predicting eBay Prices: Selecting and Interpreting Machine Learning Models – Results of the AG DANK 2018 Data Science Competition

The annual meeting of the work group on data analysis and numeric classification (DANK) took place at Stralsund University of Applied Sciences, Germany on October 26h and 27h, 2018 with a focus theme on interpretable machine learning. Traditionally, the conference is accompanied by a data science competition where the participants are invited to analyze one or several data sets and compare and discuss their solutions. In 2018, the task was to predict end prices of eBay auctions. The paper describes the task as well as a discussion of the results as provided by the conference participants. These cover aspects of preprocessing, comparison of different models, task specific hyperparameter tuning as well as the interpretation of the resulting models and the relevance of additional text information

Cluster Validation for Mixed-Type Data

For cluster analysis based on mixed-type data (i.e. data consisting of numerical and categorical variables), comparatively few clustering methods are available. One popular approach to deal with this kind of problems is an extension of the k-means algorithm (Huang, 1998), the so-called k-prototype algorithm, which is implemented in the R package clustMixType (Szepannek and Aschenbruck, 2019). It is further known that the selection of a suitable number of clusters k is particularly crucial in partitioning cluster procedures. Many implementations of cluster validation indices in R are not suitable for mixed-type data. This paper examines the transferability of validation indices, such as the Gamma index, Average Silhouette Width or Dunn index to mixed-type data. Furthermore, the R package clustMixType is extended by these indices and their application is demonstrated. Finally, the behaviour of the adapted indices is tested by a short simulation study using different data scenarios

Recent progress in turbine blade and compressor blisk regeneration

The regeneration process of jet engines is a highly complex, expensive and time-consuming. Especially the regeneration of high pressure turbine blades and compressor blisks are at the border of what is technically feasible. These components are highly loaded and thus substantial wear occurs. The blades and blisks must be overhauled or replaced regularly. The existing repair methods for these parts are inflexible and cannot be applied in many cases, resulting in a large number of scrapped parts. Therefore a new turbine blade regeneration process is presented. The goal of the improved process is to reduce the scrap rate and cost. This process includes an early evaluation of the condition of the hot-gas path components before disassembly, new detection methods for defects on the turbine blades surfaces, and more flexible manufacturing processes. The process is supported by production process simulations and functional simulations to predict the optimal regeneration path depending on the blade condition and the business model of the customer. The paper also presents a new approach for compressor blisk regeneration. This process will be developed and validated in the next years. New challenges in structural mechanics, aerodynamics, and manufacturing must be addressed due to the complexity of blisks. As part of the ongoing research, three new blisks will be designed and subjected to the complete regeneration path, which is also supported by simulations. In order to validate the simulations, their results will be compared to experimental results of the regenerated components on a compressor test rig.DFG/SFB/87

Green Segment Routing for Improved Sustainability of Backbone Networks

Improving the energy efficiency of Internet Service Provider (ISP) backbone networks is an important objective for ISP operators. In these networks, the overall traffic load throughout the day can vary drastically, resulting in many backbone networks being highly overprovisioned during periods of lower traffic volume. In this paper, we propose a new Segment Routing (SR)-based optimization algorithm that aims at reducing the energy consumption of networks during such low-traffic periods. It uses the traffic steering capabilities of SR to remove traffic from as many links as possible to allow the respective hardware components to be switched off. Furthermore, it simultaneously ensures that solutions comply to additional operator requirements regarding the overall Maximum Link Utilization in the network. Based on data from a Tier-1 ISP and a public available dataset, we show that our approach allows for up to 70 % of the overall linecards to be switched off, corresponding to an around 56% reduction of the overall energy consumption of the network in times of low traffic demands.Comment: This work has been submitted to IEEE for possible publication. Copyright may be transferred without notic

Green Traffic Engineering by Line Card Minimization

Green Traffic Engineering encompasses network design and traffic routing strategies that aim at reducing the power consumption of a backbone network. We argue that turning off linecards is the most effective approach to reach this goal. Thus, we investigate the problem of minimizing the number of active line cards in a network while simultaneously allowing a multi-commodity flow being routed and keeping the maximum link utilization below a certain threshold. In addition to proving this problem to be NP-hard, we present an optimal ILP-based algorithm as well as a heuristic based on 2-Segment Routing. Lastly, we evaluate both approaches on real-world networks obtained from the Repetita Framework and a globally operating Internet Service Provider. The results of this evaluation indicate that our heuristic is not only close to optimal but significantly faster than the optimal algorithm, making it viable in practice.Comment: This work has been submitted to IEEE for possible publication. Copyright may be transferred without notic

Critical Clearing Time Estimates of Power Grid Faults via a Set-Based Method

This paper is concerned with estimating critical clearing times in the transient stability problem of power grids without extensive time-domain simulations. We consider a highdimensional post-fault system (the grid after the fault is cleared) which we decouple into many smaller subsystems. Then, for each subsystem, we find the so-called safety sets and simulate the faulted system once to deduce the so-called safe and unsafe critical clearing times, which specify the intervals of time over which the fault may remain active before safety is compromised. We demonstrate the approach with a numerical example involving the IEEE 14 bus system.Comment: 6 pages, 3 figures. Paper is under revie