The present bachelor theses discusses the creation process of a framework for the sys-tematic analysis of twitter posts regarding their sentiment. The result is an application, which links and uses the covered theoretical approaches for text classification.

The task of object detection in the automotive sector can be performed by evaluating various sensor data. The evaluation of LiDAR data for the detection of objects is a special challenge for which systems with neural networks can be used. These neural networks are trained by means of a data set. If you want to use the net with your own recordings or another data set, it is important to know how well these systems work in combination with data from another sensor. This allows the results to be estimated in advance and compared with the results of previous experiments. In this work the sensor dependence of a LiDAR based object recognition with neural networks will be analysed. The detector used in this work is PointRCNN [1], which was designed for the KITTI dataset [2]. To check the sensor dependency, the ’AEV Autonomous Driving Dataset’ (A2D2) dataset [3] was selected as a further dataset. After an introduction to PointRCNN and its functionality, the data of both datasets are analysed. Then the data of the second dataset will be ported into the format of the KITTI dataset so that they can be used with PointRCNN. Through experiments with varying combinations of training and validation data it shall be investigated to what extent trained models can be transferred to other sensor data or datasets. Therefore, it shall be investigated how strong the dependence of the detector (PointRCNN) on the used sensors is. The results show that PointRCNN can be evaluated with a different dataset than the training dataset while still being able to detect objects. The point density of the datasets plays a decisive role for the quality of the detection. Therefore it can be said that PointRCNN has a sensor dependency that varies with the nature of the point cloud and its density. Keywords: LiDAR data, 3D object recognition, laser scanner, sensor dependency, PointRCNN, PointNet++, PointNet, KITTI Dataset, AEV Autonomous Driving Dataset, A2D2 Dataset

Learning the German language is one of the most critical challenges for refugee children in Germany. It is a prerequisite to allow communication and integration into the educational system. To solve the underlying problem, we conceptualized a set of principles for the design of language learning systems to support collaboration between teachers and refugee children, using a Design Science Research approach. The proposed design principles offer functional and non-functional requirements of systems, including the integration of open educational resources, different media types to develop visual and audio narratives that can be linked to the cultural and social background. This study also illustrates the use of the proposed design principles by providing a working prototype of a learning system. In this, refugee children can learn the language collaboratively and with freely accessible learning resources. Furthermore, we discuss the proposed design principles with various socio-technical aspects of the well-being determinants to promote a positive system design for different cultural and generational settings. Overall, despite some limitations, the implemented design principles can optimize the potential of open educational resources for the research context and derive further recommendations for further research.

IEEE Workshop 2019 - Industrial and Medical Measurement and Sensor Technology, Vehicle Sensor Technology

Vortragsreihe der Hochschule Ruhr West Kurzfassung der Beiträge zum IEEE Workshop 2014 Industrielle Messtechnik & Kraftfahrzeugsensorik 28. März 2014, Band 3

The process automation extends over every manufacturing step of a product in the steel-mill to increase the quality, quantity and energy efficiency. The product dimensions are an important part of the quality control; these must maintain the specified tolerances. Additional to the cross-sectional-area, the measured data contains much more information about the manufacturing process, e.g. eccentricity, condition of the rolls and defects of the rod. For analyzing the measured data and to gather more information about the manufacturing process it is necessary to increase the speed of the data acquisition by performing some modifications of the VNA, e.g. faster analog to digital converter and microcontroller, improved firmware and optimized values of the passive electrical components for faster time constants and transient responses.

Quality and dimensional accuracy of hot rolled steel rods depend on several process parameters. In fact many of these crucial parameters are not be sufficiently determined yet. By improving automation and process control costs and scrap of production can be decreased. As part of the research project PIREF, one of these parameters – the roll gap – is under investigation beside other topics. Before starting rolling, the roll gap is typically set to a fixed value according to the planed dimensions of the product, but the forces during the rolling of the rod cause an enlargement of the roll gap. In which way the rolls change their position and form shall be examined in our research project. Therefore a first experimental setup has been built up to determine the change in position of the rolls under applied force. This is realized by a pot core coil as sensor using impedance analysis. The first results are presented in this work as a proof-of-principle.

In the field of producing hot-rolled steel bars and wires, hot rolling mills are incomplete or barely equipped with measuring technology for recording relevant process parameters. Therefore, there is a big potential to increase product quality and to decrease costs and scrap by improving process control establishing new sensor systems. One of these crucial parameters is the roll gap,which is investigated as part of the research project PIREF. In this paper an experimental setup for examining the roll gap during a rolling process is presented and based on these results different sensor arrangements are discussed.

The development of innovative measuring technology for process optimization in hot rolling mills becomes more and more relevant because of increasing demands on product quality. Measurement technology for high-resolution non-contact cross-sectional area measurement has shown that the variation in cross-sectional area contains information about the rolling process. This information can be used for the development of new measurement devices and analytical methods for process optimization. The harsh environmental conditions and strict safety regulations result in great effort when implementing a new sensor prototype in hot rolling mills. For this reason, this work presents a mechatronic test stand that can simulate the cross-sectional area variation under laboratory conditions realistically.

Globalization and information technology enable people to join the movement of global citizenship and work without borders. However, different type of barriers existed that could affect collaboration in today’s work environment, in which different generations are involved. Although researchers have identified several technical barriers to intergenerational collaboration (iGOAL), the influence of cultural diversity on iGOAL has rarely been studied. Therefore, using a quantitative study approach, this paper investigates the impact of differences in cultural background on perceived technical and operational barriers to iGOAL. Our study reveals six barriers to IGC that are perceived differently by culturally diverse people (CDP) and non-CDP. Furthermore, CDP can foster IGC because CDP consider the barriers to be of less of a reason to avoid working with different generations than do non-CDP.