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Optimization via Multimodel Simulation
Get PDFIncreasing computational power and the availability of 3D printers provide new tools for the combination of modeling and experimentation. Several simulation tools can be run independently and in parallel, e.g., long running computational fluid dynamics simulations can be accompanied by experiments with 3D printers. Furthermore, results from analytical and data-driven models can be incorporated. However, there are fundamental differences between these modeling approaches: some models, e.g., analytical models, use domain knowledge, whereas data-driven models do not require any information about the underlying processes.
At the same time, data-driven models require input and output data, but analytical models do not. Combining results from models with different input-output structures might improve and accelerate the optimization process. The optimization via multimodel simulation (OMMS) approach, which is able to combine results from these different models, is introduced in this paper.
Using cyclonic dust separators as a real-world simulation problem, the feasibility of this approach is demonstrated and a proof-of-concept is presented. Cyclones are popular devices used to filter dust from the emitted flue gases. They are applied as pre-filters in many industrial processes including energy production and grain processing facilities. Pros and cons of this multimodel optimization approach are discussed and experiences from experiments are presented
Bewertung des verfügbaren Kapitals am Beispiel des Datenmodells der „IVW Privat AG“
Get PDFMit der „IVW Privat AG“ liegt ein relativ durchgängiges Datenmodell eines Schadenversicherungs-unternehmens vor, mit dem in vorangegangenen Publikationen eine Vielzahl von Solvency II Anwen-dungen illustriert werden konnten. Ergänzend dazu sollen in dieser Publikation unterschiedliche Bewertungsansätze für das verfügbare Kapital vorgestellt und miteinander verglichen werden – ausgehend vom sicherheitsorientierten HGB Kapital bis hin zum Marktkonsistenten Embedded Value (MCEV).The data model of the so-called “IVW Privat AG” provides a consistent model to demonstrate various Solvency II applications that have been illustrated in different publications before. Additionally in this paper, different valuation approaches should be presented and compared – starting from the security based German GAAP and finalizing with the Market Consistent Embedded Value (MCEV)
Is Social Learning More Than Parameter Tuning?
Get PDFSocial learning enables multiple robots to share learned experiences while completing a task. The literature offers examples where robots trained with social learning reach a higher performance compared to their individual learning counterparts. No explanation has been advanced for that observation. In this research, we present experimental results suggesting that a lack of tuning of the parameters in social learning experiments could be the cause. In other words: the better the parameter settings are tuned, the less social learning can improve the system performance
Recovery after extreme events - Lessons learned and remaining challenges in Disaster Risk Reduction
Get PDFDisasters such as the Indian Ocean Tsunami 2004, but also other extreme events such as cyclones, earthquakes and tsunami substantially affect the lives of many thousands of people - they are events radically and abruptly changing local circumstances and needs. At the same time they can significantly reshape global paradigms of Disaster Risk Reduction (DRR). Such events also bring to light the challenges in coordinating assistance from the “global community” with all the intended and un-intended effects. Two of the most pressing questions therefore are whether the different actors have learned from the disaster and whether processes of DRR and livelihood improvements have been implemented successfully.
This volume gathers selected papers addressing the following key questions:
- Lessons learned: Which lessons have been learned in a way that a difference can be seen today for the livelihoods and resilience of local people in the regions affected?
- Lessons to be Learned: Despite the body of knowledge created and reflected in a good number of lessons learned studies – what is still unsolved or needs to be emphasized?
- Monitoring and evaluation: Which DRR measures have been perpetuated and how can they be monitored and evaluated scientifically?
- Resilience effects and (unintended) side-effects: Which coping, recovery and adaptation measures are
supported by the resilience paradigm and which other areas are side-lined, neglected or even contrary to the intended effects?
- Dynamics in risk: In which cases has resilience building taken place? In which cases have ulnerabilities
been shifted internally or new vulnerabilities been created?
- Relocation/resettlement: How did the relocation/resettlement process of displaced people take place and what are its long-term effects?
- Urban-rural divide: How have DRR measures in urban vs. rural areas differed and which linkages but also rifts in rehabilitation and reconstruction initiatives can be observed between the two?
- Early warning: What is the future of Early Warning and how can important top-down information chains benefit from or be balanced with bottom-up feedback of users and affected people?
It appears that extreme disaster events spark a plethora of actions in academia, civil society, media, policy, private sector and other organisations. Tragic, as such disasters are, they offer incentives for learning, locally and globally. Lately, disaster impacts have in many cases been detracted through the application of knowledge and experience gained from previous events. However, there are still a number of challenges with regards to learning from past disaster
Simulation-based Test Functions for Optimization Algorithms
Get PDFWhen designing or developing optimization algorithms, test functions are crucial to evaluate
performance. Often, test functions are not sufficiently difficult, diverse, flexible or relevant to real-world
applications. Previously,
test functions with real-world relevance were generated by training a machine learning model based on
real-world data. The model estimation is used as a test function.
We propose a more principled approach using simulation instead of estimation.
Thus, relevant and varied test functions
are created which represent the behavior of real-world fitness landscapes.
Importantly, estimation can lead to excessively smooth test functions
while simulation may avoid this pitfall. Moreover, the simulation
can be conditioned by the data, so that the simulation reproduces the training data
but features diverse behavior in unobserved regions of the search space.
The proposed test function generator is illustrated with an intuitive, one-dimensional
example. To demonstrate the utility of this approach it
is applied to a protein sequence optimization problem.
This application demonstrates the advantages as well as practical limits of simulation-based
test functions
Surrogate-Assisted Learning of Neural Networks
Get PDFSurrogate-assisted optimization has proven to be very successful if applied to industrial problems. The use of a data-driven surrogate model of an objective function during an optimization cycle has many bene ts, such as being cheap to evaluate and further providing both information about the objective landscape and the parameter space. In preliminary work, it was researched how surrogate-assisted optimization can help to optimize the structure of a neural network (NN) controller. In this work, we will focus on how surrogates can help to improve the direct learning process of a transparent feed-forward neural network controller. As an initial case study we will consider a manageable real-world control task: the elevator supervisory group problem (ESGC) using a simplified simulation model. We use this model as a benchmark which should indicate the applicability and performance of surrogate-assisted optimization to this kind of tasks. While the optimization process itself is in this case not onsidered expensive, the results show that surrogate-assisted optimization is capable of outperforming metaheuristic optimization methods for a low number of evaluations. Further the surrogate can be used for signi cance analysis of the inputs and weighted connections to further exploit problem information
Alternative Capital und Basisrisiko in der Standardformel (non-life) von Solvency II
Get PDFDas prinzipienorientierte Aufsichtssystem von Solvency II erkennt als zentralen Grundsatz, dass nach dem Prinzip „Substanz über Form“ die ökonomische Wirkung eines Risikotransferinstrumentes und nicht die formale Einbettung desselben als Entscheidungskriterium der Berücksichtigungsfähigkeit gilt. Dieser Grundsatz trägt den Entwicklungen auf dem Rückversicherungsmarkt insoweit Rechnung, da dadurch auch alternative Formen des vt. Risikotransfers grundsätzlich Anerkennung finden können, wenn sie den Anerkennungsvoraussetzungen der aufsichtsrechtlichen Vorgaben entsprechen. Dabei zeigt sich, dass der Aufbau und die Mechanik dieser alternativen Formen des vt. Risikotransfer insbesondere eine (ökonomisch) abweichende Bewertung hinsichtlich des vt. Basisrisikos und Ausfallrisikos bedingen können. Kern der vorliegenden Arbeit ist deshalb die Prüfung, inwieweit die Vorgaben von Solvency II diese Unterschiedlichkeit zur Berücksichtigung von vt. Basisrisiko ökonomisch adäquat abbilden. Dabei wird dargestellt, dass insbesondere eine nach Solvency II im Vergleich zum Marktverständnis weit gefasste Definition der Begrifflichkeit sowie eine uneinheitliche Anwendung innerhalb der Gesetzestexte der einheitlichen Berücksichtigung potentiell zuwiderlaufen oder uneinheitliche Prüfungserfordernisse an ökonomisch gleich wirkende Instrumente stellen. Darüber wird hergeleitet, dass die Vorgaben nach Solvency II Regelungen enthalten, welche die ökonomische Wirkung des vt. Basisrisikos (z. B. aus Währungsinkongruenzen) inadäquat widerspiegeln.Following the leading principle "substance over form" Solvency II recognizes that the economic effect of a risk transfer instrument and not the formal structure should be considered for a decision to assess the applicability of a certain risk transfer instrument. Hence, this principle also reflects developments in the reinsurance market as alternative forms of risk transfer can be recognized, if they meet the respective regulatory conditions. However, the structure and mechanics of alternative forms of risk transfer can imply a deviating economic valuation of these instruments, especially with regards to basis and default risks. Therefore, the core of this work is to examine the extent to which the requirements of Solvency II adequately reflect this deviation to account for basic risks of the underlying risk transfer instrument. We find that, a broader definition of Solvency II of the term in comparison to the market understanding as well as a non-uniform application within the legal texts potentially conflict with the interest of uniform application or impose unequal test requirements on economically equivalent instruments. In addition, it is deduced that Solvency II contains provisions that do not adequately reflect the economic impact of the underlying risk (e. g. from currency mismatches)
Conditional Inference Trees for the Knowledge Extraction from Motor Health Condition Data
Get PDFAs 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 manne
In a Nutshell: Sequential Parameter Optimization
Get PDFThe performance of optimization algorithms relies crucially on their parameterizations. Finding good parameter settings is called algorithm tuning. Using
a simple simulated annealing algorithm, we will demonstrate how optimization algorithms can be tuned using the Sequential Parameter Optimization Toolbox (SPOT). SPOT provides several tools for automated and interactive tuning. The underlying concepts of the SPOT approach are explained. This includes key techniques such as exploratory fitness landscape analysis and response surface methodology. Many examples illustrate
how SPOT can be used for understanding the performance of algorithms and gaining insight into algorithm behavior. Furthermore, we demonstrate how SPOT can be used as an optimizer and how a sophisticated ensemble approach is able to combine several meta models via stacking
Forschungsbericht 2016 - Fakultät für Wirtschafts- und Rechtswissenschaften der Technischen Hochschule Köln
Get PDFDie Fakultät für Wirtschafts- und Rechtswissenschaften der Technischen Hochschule Köln möchte mit diesem Bericht dokumentieren, mit welchen Problemstellungen sich die forschenden Kolleginnen und Kollegen der Fakultät im Jahr 2016 auseinander gesetzt haben