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Data pre-processing is a key research topic in data mining because it plays a
crucial role in improving the accuracy of any data mining algorithm. In most
real world cases, a significant amount of the recorded data is found missing
due to most diverse errors. This loss of data is nearly always unavoidable.
Recovery of missing data plays a vital role in avoiding inaccurate data
mining decisions. Most multivariate imputation methods are not compatible
to univariate datasets and the traditional univariate imputation techniques
become highly biased as the missing data gap increases. With the current
technological advancements abundant data is being captured every second.
Hence, we intend to develop a new algorithm that enables maximum
utilization of the available big datasets for imputation. In this paper, we
present a Seasonal and Trend decomposition using Loess (STL) based
Seasonal Moving Window Algorithm, which is capable of handling patterns
with trend as well as cyclic characteristics. We show that the algorithm is
highly suitable for pre-processing of large datasets.
Das Rathaus Rodenkirchen im Kölner Süden wurde 1967 nach einem Entwurf des Kölner Architekten Walther Ruoff (1914-1991) erbaut. Er war 1962 als Gewinner aus einem Wettbewerb für ein neues repräsentatves Rathaus der damals eigenständigen Gemeinde hervorgegangen. Es steht bis zuletzt als bauliches Zeugnis von Wachstum und Selbstbewusstsein in der Architektursprache des Brutalismus. 2005 wurde nach langen Debatten der Abriss des Hauses beschlossen. Die Publikation beschäftigt sich mit der Architektur und Bauidee des Rathauses, wie auch mit der Rezeptionsgeschichte, die letztendlich zum bedauerlichen Abbruch führte.
This volume addresses the topics of Disaster Risk Reduction (DRR) and Climate Change Adaption (CCA) from the perspective of participants of the DAAD Alumni Summer School 2013. Contributions from 16 countries are gathered in this volume, thereby sharing specific knowledge on climate extremes, disasters, adaptation and prevention measures as well as current strategies in a range of different national contexts. The DAAD Alumni Summer School opened up a forum for integrative thinking and learning across cultures, disciplines and institutions. This volume is directly linked to the first volume of the series and presents a further outcome of the Summer School 2013. It invites the reader to look beyond common perspectives of DRR and CCA and relates climate change and natural disasters with interdisciplinary and bottom-up policy making. The outcomes presented in the two volumes are a starting point for further international & transdisciplinary knowledge exchange activities planned for the upcoming years.
In facing recent natural and man-made disasters Disaster Risk Reduction (DRR) and Climate Change Adaption (CCA) calls for integrative thinking and learning across cultures, disciplines and institutions. In times of increasing complexity, insecurity and uncertainty thinking outside the box becomes essential. This first volume of “Integrative Risk and Security Research” presents related research contributions developed in the context of the 2013 DAAD Alumni Summer School "Coping with Disasters and Climate Extremes - Challenges & Cooperation Potential". It invites the reader to look beyond common perspectives of DRR and CCA and relates climate change and natural disasters with interdisciplinary and bottom-up policy making.
Benchmark experiments are required to test, compare, tune, and understand optimization algorithms. Ideally, benchmark problems closely reflect real-world problem behavior. Yet, real-world problems are not always readily available for benchmarking. For example, evaluation costs may be too high, or resources are unavailable (e.g., software or equipment). As a solution, data from previous evaluations can be used to train surrogate models which are then used for benchmarking. The goal is to generate test functions on which the performance of an algorithm is similar to that on the real-world objective function. However, predictions from data-driven models tend to be smoother than the ground-truth from which the training data is derived. This is especially problematic when the training data becomes sparse. The resulting benchmarks may not reflect the landscape features of the ground-truth, are too easy, and may lead to biased conclusions.
To resolve this, we use simulation of Gaussian processes instead of estimation (or prediction). This retains the covariance properties estimated during model training. While previous research suggested a decomposition-based approach for a small-scale, discrete problem, we show that the spectral simulation method enables simulation for continuous optimization problems. In a set of experiments with an artificial ground-truth, we demonstrate that this yields more accurate benchmarks than simply predicting with the Gaussian process model.
As the amount of data gathered by monitoring systems increases, using computational tools to analyze it becomes a necessity.
Machine learning algorithms can be used in both regression and classification problems, providing useful insights while avoiding the bias and proneness to errors of humans. In this paper, a specific kind of decision tree algorithm, called conditional inference tree, is used to extract relevant knowledge from data that pertains to electrical motors. The model is chosen due to its flexibility, strong statistical foundation, as well as great capabilities to generalize and cope with problems in the data. The obtained knowledge is organized in a structured way and then analyzed in the context of health condition monitoring. The final
results illustrate how the approach can be used to gain insight into the system and present the results in an understandable, user-friendly manner
The availability of several CPU cores on current computers enables
parallelization and increases the computational power significantly.
Optimization algorithms have to be adapted to exploit these highly
parallelized systems and evaluate multiple candidate solutions in
each iteration. This issue is especially challenging for expensive
optimization problems, where surrogate models are employed to
reduce the load of objective function evaluations.
This paper compares different approaches for surrogate modelbased
optimization in parallel environments. Additionally, an easy
to use method, which was developed for an industrial project, is
proposed. All described algorithms are tested with a variety of
standard benchmark functions. Furthermore, they are applied to
a real-world engineering problem, the electrostatic precipitator
problem. Expensive computational fluid dynamics simulations are
required to estimate the performance of the precipitator. The task
is to optimize a gas-distribution system so that a desired velocity
distribution is achieved for the gas flow throughout the precipitator.
The vast amount of possible configurations leads to a complex
discrete valued optimization problem. The experiments indicate
that a hybrid approach works best, which proposes candidate solutions
based on different surrogate model-based infill criteria and
evolutionary operators.
Collective Defined Contribution Plans – Backtesting Based on German Capital Market Data 1950 - 2022
(2022)
Using historical capital market data for Germany (1950-2022) we analyze and compare (individual) defined contribution (IDC-) and collective defined contribution (CDC) pension plans. To this end we define simple asset liability management rules that govern a CDC pension plan and compare these to IDC-plans with the same asset allovation. Our main result is, that the CDC pension plans allow for a significant improvement of the risk return profile compared to individual pension plans. Hereby we consider different risk measures. This empirical study affirms the theoretical results based on stochastic CDC-models.
Dieser Schlussbericht beschreibt die im Projekt „CI-basierte mehrkriterielle Optimierungsverfahren für Anwendungen in der Industrie“ (CIMO) im Zeitraum von November 2011 bis einschließlich Oktober 2014 erzielten Ergebnisse. Für aufwändige Optimierungsprobleme aus der Industrie wurden geeignete Lösungsverfahren entwickelt. Der Schwerpunkt lag hierbei auf Methoden aus den Bereichen Computational Intelligence (CI) und Surrogatmodellierung. Diese bieten die Möglichkeit, wichtige Herausforderung von aufwändigen, komplexen Optimierungsproblemen zu lösen. Die entwickelten Methoden können verschiedene konfliktäre Zielgrößen berücksichtigen, verschiedene Hierarchieebenen des Problems in die Optimierung integrieren, Nebenbedingungen beachten, vektorielle aber auch strukturierte Daten verarbeiten (kombinatorische Optimierung) sowie die Notwendigkeit teurer/zeitaufwändiger Zielfunktionsberechnungen reduzieren. Die entwickelten Methoden wurden schwerpunktmäßig auf einer Problemstellung aus der Kraftwerkstechnik angewendet, nämlich der Optimierung der Geometrie eines Fliehkraftabscheiders (auch: Zyklon), der Staubanteile aus Abgasen filtert. Das Optimierungsproblem, das diese FIiehkraftabscheider aufwerfen, führt zu konfliktären Zielsetzungen (z.B. Druckverlust, Abscheidegrad). Zyklone können unter anderem über aufwändige Computational Fluid Dynamics (CFD) Simulationen berechnet werden, es stehen aber auch einfache analytische Gleichungen als Schätzung zu Verfügung. Die Verknüpfung von beidem zeigt hier beispielhaft wie Hierarchieebenen eines Optimierungsproblems mit den Methoden des Projektes verbunden werden können. Neben dieser Schwerpunktanwendung konnte auch gezeigt werden, dass die Methoden in vielen weiteren Bereichen Erfolgreich zur Anwendung kommen können: Biogaserzeugung, Wasserwirtschaft, Stahlindustrie. Die besondere Herausforderung der behandelten Probleme und Methoden bietet viele wichtige Forschungsmöglichkeiten für zukünftige Projekte, die derzeit durch die Projektpartner vorbereitet werden.