Bauhaus University, Weimar

About: Bauhaus University, Weimar is a education organization based out in Weimar, Thüringen, Germany. It is known for research contribution in the topics: Finite element method & Isogeometric analysis. The organization has 1421 authors who have published 2998 publications receiving 104454 citations. The organization is also known as: Bauhaus-Universität Weimar & Hochschule für Architektur und Bauwesen.

Adaptive training of video sets for image recognition on mobile phones

Abstract: We present an enhancement towards adaptive video training for PhoneGuide, a digital museum guidance system for ordinary camera-equipped mobile phones. It enables museum visitors to identify exhibits by capturing photos of them. In this article, a combined solution of object recognition and pervasive tracking is extended to a client---server-system for improving data acquisition and for supporting scale-invariant object recognition. A static as well as a dynamic training technique are presented that preprocess the collected object data differently and apply two types of neural networks (NN) for classification. Furthermore, the system enables a temporal adaptation for ensuring a continuous data acquisition to improve the recognition rate over time. A formal field experiment reveals current recognition rates and indicates the practicability of both methods under realistic conditions in a museum.

Volumetric parametrization from a level set boundary representation with PHT-splines ☆

Abstract: A challenge in isogeometric analysis is constructing analysis-suitable volumetric meshes which can accurately represent the geometry of a given physical domain. In this paper, we propose a method to derive a spline-based representation of a domain of interest from voxel-based data. We show an efficient way to obtain a boundary representation of the domain by a level-set function. Then, we use the geometric information from the boundary (the normal vectors and curvature) to construct a matching C 1 representation with hierarchical cubic splines. The approximation is done by a single template and linear transformations (scaling, translations and rotations) without the need for solving an optimization problem. We illustrate our method with several examples in two and three dimensions, and show good performance on some standard benchmark test problems.

Comparative Performance Tests and Validation of NDT Methods for Concrete Testing

Abstract: Validation of non-destructive testing methods is necessary to create a common basis where different systems can be compared and their applications and limitations be identified. This can be achieved through comparing the measurements taken by several systems used for a common diagnostic purpose under practical but controlled testing conditions. Well-designed small and large laboratory or field specimens promise such conditions. The special concrete specimen (LCS) at BAM was constructed for validation purposes, in particular, to be used for evaluating the performance of echo methods. The thickness of the specimen is varying and it contains carefully designed built-in faults, such as voids, honeycombs and tendon ducts with various degrees of grouting defects. Since the geometry and condition of the defects are known, it can be used to compare the performance of radar, ultrasonic, impact-echo. The research was conducted within the Research group FOR384, sponsored by the German Research Society DFG.

Comparative Analysis of Artificial Intelligence Models for Accurate Estimation of Groundwater Nitrate Concentration.

Abstract: Prediction of the groundwater nitrate concentration is of utmost importance for pollution control and water resource management. This research aims to model the spatial groundwater nitrate concentration in the Marvdasht watershed, Iran, based on several artificial intelligence methods of support vector machine (SVM), Cubist, random forest (RF), and Bayesian artificial neural network (Baysia-ANN) machine learning models. For this purpose, 11 independent variables affecting groundwater nitrate changes include elevation, slope, plan curvature, profile curvature, rainfall, piezometric depth, distance from the river, distance from residential, Sodium (Na), Potassium (K), and topographic wetness index (TWI) in the study area were prepared. Nitrate levels were also measured in 67 wells and used as a dependent variable for modeling. Data were divided into two categories of training (70%) and testing (30%) for modeling. The evaluation criteria coefficient of determination (R2), mean absolute error (MAE), root mean square error (RMSE), and Nash-Sutcliffe efficiency (NSE) were used to evaluate the performance of the models used. The results of modeling the susceptibility of groundwater nitrate concentration showed that the RF (R2 = 0.89, RMSE = 4.24, NSE = 0.87) model is better than the other Cubist (R2 = 0.87, RMSE = 5.18, NSE = 0.81), SVM (R2 = 0.74, RMSE = 6.07, NSE = 0.74), Bayesian-ANN (R2 = 0.79, RMSE = 5.91, NSE = 0.75) models. The results of groundwater nitrate concentration zoning in the study area showed that the northern parts of the case study have the highest amount of nitrate, which is higher in these agricultural areas than in other areas. The most important cause of nitrate pollution in these areas is agriculture activities and the use of groundwater to irrigate these crops and the wells close to agricultural areas, which has led to the indiscriminate use of chemical fertilizers by irrigation or rainwater of these fertilizers is washed and penetrates groundwater and pollutes the aquifer.