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Increasing 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.
Recovery after extreme events - Lessons learned and remaining challenges in Disaster Risk Reduction
(2017)
Disasters 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 disasters
The 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.
Surrogate-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.
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
When 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.
Social 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.