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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.
Das Ziel der vorliegenden Bachelorarbeit ist die Konzeption eines neuen Ansatzes − die Positive Co-Creation −, der die Elemente des Positive Computing in die Co-Creation integriert. Dafür wurden in einer Literaturanalyse die bestehenden Schwachstellen der Co-Creation herausgearbeitet, um anschließend die Vorteile des Positive Computing aufzuzeigen. Nach der Entwicklung eines spezifischen Modells der Positive Co-Creation, inklusive der verwendeten Methoden und deren Auswirkungen auf die Wohlbefindensfaktoren, wurde das Modell anhand von Experteninterviews evaluiert und verbessert. Das Ergebnis dieser Arbeit ist ein theoretisches Modell der Positive Co-Creation, welches den Prozess vollständig abbildet und einen Ansatzpunkt für eine praktische Umsetzung bildet. Dieser Ansatz ist gut geeignet, um bestehende Co-Creation-Prozesse anhand von Technologien um die Aspekte des Wohlbefindens zu erweitern.
The highly successful lecture series on the topic of measurement and sensor technologies as part of the IEEE Workshop at the University of Applied Sciences Ruhr West (HRW) is being continued in collaboration with the University of Siegen, the TU Chemnitz and the ITMO National Research University of Information Technologies, Mechanics and Optics in St. Petersburg. This time the event is featuring an even more international orientation by linking it with the Russian SENSORICA. The topics cover industrial and medical measurement technology as well as sensor technology in vehicles. Our event offers a platform for knowledge transfer between industry and public and commercial research institutions in the area of measurement technology.
This Abstract Book offers the opportunity of contacting speakers even after the event.
In addition we are very pleased to have selected contributions published in a special edition of the journal „tm Technisches Messen“ (De Gruyter Oldenbourg Verlag) again this year.
The detection of soil erosion processes in dams, hydraulic heave failure or corrosion processes of reinforcing steel in concrete are a small selection of measuring applications in civil engineering where the impedance analysis can be used to determine the measurand. Those measuring applications are having high requirements for the measuring hardware. For example a common interface for fast data exchange, high resolution, independent functionality and easy customizability to suit the measuring application. For that reason, a well-known application for steel-mill process monitoring can be used as a development platform. This hardware platform is based on a vector network analyzer and is meeting the requirements mainly. However, a couple of modifications has to be made, like replacing the ADC for a higher sample rate, Ethernet for easy and fast data exchange and the microcontroller for more calculation power.
