Search results for: Maik Meusel

Authors: Vanessa Kummer, Maik Meusel

Abstract:

Real estate demand estimates are typically derived from transaction data. However, in regions with excess demand, transactions are driven by supply and therefore do not indicate what people are actually looking for. To estimate the demand for housing in Switzerland, search subscriptions from all important Swiss real estate platforms are used. These data do, however, suffer from missing information—for example, many users do not specify how many rooms they would like or what price they would be willing to pay. In economic analyses, it is often the case that only complete data is used. Usually, however, the proportion of complete data is rather small which leads to most information being neglected. Also, the data might have a strong distortion if it is complete. In addition, the reason that data is missing might itself also contain information, which is however ignored with that approach. An interesting issue is, therefore, if for economic analyses such as the one at hand, there is an added value by using the whole data set with the imputed missing values compared to using the usually small percentage of complete data (baseline). Also, it is interesting to see how different algorithms affect that result. The imputation of the missing data is done using unsupervised learning. Out of the numerous unsupervised learning approaches, the most common ones, such as clustering, principal component analysis, or neural networks techniques are applied. By training the model iteratively on the imputed data and, thereby, including the information of all data into the model, the distortion of the first training set—the complete data—vanishes. In a next step, the performances of the algorithms are measured. This is done by randomly creating missing values in subsets of the data, estimating those values with the relevant algorithms and several parameter combinations, and comparing the estimates to the actual data. After having found the optimal parameter set for each algorithm, the missing values are being imputed. Using the resulting data sets, the next step is to estimate the willingness to pay for real estate. This is done by fitting price distributions for real estate properties with certain characteristics, such as the region or the number of rooms. Based on these distributions, survival functions are computed to obtain the functional relationship between characteristics and selling probabilities. Comparing the survival functions shows that estimates which are based on imputed data sets do not differ significantly from each other; however, the demand estimate that is derived from the baseline data does. This indicates that the baseline data set does not include all available information and is therefore not representative for the entire sample. Also, demand estimates derived from the whole data set are much more accurate than the baseline estimation. Thus, in order to obtain optimal results, it is important to make use of all available data, even though it involves additional procedures such as data imputation.

Keywords: demand estimate, missing-data imputation, real estate, unsupervised learning

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Authors: Paul Gurr, Maik Moser

Abstract:

In Europe, some countries recently abolished air passenger taxes (APT), while others issued or consider issuing an APT. From a fiscal perspective, APT can benefit the environment, while generating a vast amount of tax revenue with relatively low administration costs. However, they may have significant negative effects on the economy. Focusing on the German air passenger tax issued 2011, this work estimates the elasticity of tourism outflows using data on passenger departures from German airports between 2010 and 2016 aggregated by destination country. The results are obtained by estimating a model of the demand for outbound tourism. In line with theory, the regression results indicate a negative relationship between taxes and departures from Germany. Furthermore, on average, an increase of the air passenger tax rate results in a relatively higher decrease of passenger departures. The elasticity of tourism outflows can be used to estimate tax revenue changes and hence evaluate possible policy actions. Neglecting environmental reasons, the results suggest that tax revenue might be maximized by reducing the air passenger tax rate. Besides Germany, this work is also important for countries which have or consider implementing APT.

Keywords: air passenger tax, Germany, Outbound tourism, panel data

Procedia PDF Downloads 265

Authors: Wei-Shan Wang, Klaus Vogel, Felix Gabler, Maik Wiemer, Thomas Gessner

Abstract:

Metallic glasses, which are free of grain boundaries, have superior properties including large elastic limits, high strength, and excellent wear and corrosion resistance. Therefore, bulk metallic glasses (BMG) and thin film metallic glasses (TFMG) have been widely developed and investigated. Among various kinds of metallic glasses, Pd-Cu-Si TFMG, which has lower elastic modulus and better resistance of oxidation and corrosions compared to Zr- and Fe-based TFMGs, can be a promising candidate for MEMS applications. However, the study of Pd-TFMG membrane is still limited. This paper presents free-standing Pd-based metallic glass membranes with large area fabricated on wafer level for the first time. Properties of Pd-Cu-Si thin film metallic glass (TFMG) with various deposition parameters are investigated first. When deposited at 25°C, compressive stress occurs in the Pd76Cu6Si18 thin film regardless of Ar pressure. When substrate temperature is increased to 275°C, the stress state changes from compressive to tensile. Thin film stresses are slightly decreased when Ar pressure is higher. To show the influence of temperature on Pd-TFMGs, thin films without and with post annealing below (275°C) and within (370°C) supercooled liquid region are investigated. Results of XRD and TEM analysis indicate that Pd-TFMGs remain amorphous structure with well-controlled parameters. After verification of amorphous structure of the Pd-TFMGs, free-standing Pd-Cu-Si membranes were fabricated by depositing Pd-Cu-Si thin films directly on 200nm-thick silicon nitride membranes, followed by post annealing and dry etching of silicon nitride layer. Post annealing before SiNx removal is used to further release internal stress of Pd-TFMGs. The edge length of the square membrane ranges from 5 to 8mm. The effect of post annealing on Pd-Cu-Si membranes are discussed as well. With annealing at 370°C for 5 min, Pd-MG membranes are fully distortion-free after removal of SiNx layer. Results show that, by introducing annealing process, the stress-relief, distortion-free Pd-TFMG membranes with large area can be a promising candidate for sensing applications such as pressure and gas sensors.

Keywords: amorphous alloy, annealing, metallic glasses, TFMG membrane

Procedia PDF Downloads 327

Authors: Maik Kschischo, Dominik Kahl, Philipp Wendland, Andreas Weber

Abstract:

Understanding and modelling of real-world complex dynamic systems in biology, engineering and other fields is often made difficult by incomplete knowledge about the interactions between systems states and by unknown disturbances to the system. In fact, most real-world dynamic networks are open systems receiving unknown inputs from their environment. To understand a system and to estimate the state dynamics, these inputs need to be reconstructed from output measurements. Reconstructing the input of a dynamic system from its measured outputs is an ill-posed problem if only a limited number of states is directly measurable. A first requirement for solving this problem is the invertibility of the input-output map. In our work, we exploit the fact that invertibility of a dynamic system is a structural property, which depends only on the network topology. Therefore, it is possible to check for invertibility using a structural invertibility algorithm which counts the number of node disjoint paths linking inputs and outputs. The algorithm is efficient enough, even for large networks up to a million nodes. To understand structural features influencing the invertibility of a complex dynamic network, we analyze synthetic and real networks using the structural invertibility algorithm. We find that invertibility largely depends on the degree distribution and that dense random networks are easier to invert than sparse inhomogeneous networks. We show that real networks are often very difficult to invert unless the sensor nodes are carefully chosen. To overcome this problem, we present a sensor node placement algorithm to achieve invertibility with a minimum set of measured states. This greedy algorithm is very fast and also guaranteed to find an optimal sensor node-set if it exists. Our results provide a practical approach to experimental design for open, dynamic systems. Since invertibility is a necessary condition for unknown input observers and data assimilation filters to work, it can be used as a preprocessing step to check, whether these input reconstruction algorithms can be successful. If not, we can suggest additional measurements providing sufficient information for input reconstruction. Invertibility is also important for systems design and model building. Dynamic models are always incomplete, and synthetic systems act in an environment, where they receive inputs or even attack signals from their exterior. Being able to monitor these inputs is an important design requirement, which can be achieved by our algorithms for invertibility analysis and sensor node placement.

Keywords: data-driven dynamic systems, inversion of dynamic systems, observability, experimental design, sensor node placement

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Authors: Kai Warsoenke, Maik Mackiewicz

Abstract:

To achieve a high quality of assembled car body structures, tolerancing is used to ensure a geometric accuracy of the single car body parts. There are two main techniques to determine the required tolerances. The first is tolerance analysis which describes the influence of individually tolerated input values on a required target value. Second is tolerance synthesis to determine the location of individual tolerances to achieve a target value. Both techniques are based on classical statistical methods, which assume certain probability distributions. To ensure competitiveness in both saturated and dynamic markets, production processes in vehicle manufacturing must be flexible and efficient. The dimensional specifications selected for the individual body components and the resulting assemblies have a major influence of the quality of the process. For example, in the manufacturing of forming tools as operating equipment or in the higher level of car body assembly. As part of the metrological process monitoring, manufactured individual parts and assemblies are recorded and the measurement results are stored in databases. They serve as information for the temporary adjustment of the production processes and are interpreted by experts in order to derive suitable adjustments measures. In the production of forming tools, this means that time-consuming and costly changes of the tool surface have to be made, while in the body shop, uncertainties that are difficult to control result in cost-intensive rework. The stored measurement results are not used to intelligently design tolerances in future processes or to support temporary decisions based on real-world geometric data. They offer potential to extend the tolerancing methods through data analysis and machine learning models. The purpose of this paper is to examine real-world measurement data from individual car body components, as well as assemblies, in order to develop an approach for using the data in short-term actions and future projects. For this reason, the measurement data will be analyzed descriptively in the first step in order to characterize their behavior and to determine possible correlations. In the following, a database is created that is suitable for developing machine learning models. The objective is to create an intelligent way to determine the position and number of measurement points as well as the local tolerance range. For this a number of different model types are compared and evaluated. The models with the best result are used to optimize equally distributed measuring points on unknown car body part geometries and to assign tolerance ranges to them. The current results of this investigation are still in progress. However, there are areas of the car body parts which behave more sensitively compared to the overall part and indicate that intelligent tolerancing is useful here in order to design and control preceding and succeeding processes more efficiently.

Keywords: automotive production, machine learning, process optimization, smart tolerancing

Procedia PDF Downloads 80