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Fünfte Ordnung über die Änderung der Grundordnung der Hochschule Bonn-Rhein-Sieg vom 18.03.2015
(2015)
Unternehmensführung
(2015)
Dieses Buch führt Sie systematisch und leicht verständlich in das Thema Unternehmensführung ein. Es konzentriert sich bewusst auf die wichtigsten Fragen des Handelns als verantwortlicher Manager in der Unternehmensführung bzw. in der Bereichs- oder Abteilungsverantwortung. Dabei werden ausgewählte Probleme aus dem Management entwickelt und anschließend anhand von Praxisbeispielen erläutert. (Verlagsangaben)
Solar energy is one option to serve the rising global energy demand with low environmental Impact [1]. Building an energy system with a considerable share of solar power requires long-term investment and a careful investigation of potential sites. Therefore, understanding the impacts from varying regionally and locally determined meteorological conditions on solar energy production will influence energy yield projections. Clouds are moving on a short term timescale and have a high influence on the available solar radiation, as they absorb, reflect and scatter parts of the incoming light [2]. However, modeling photovoltaic (PV) power yields with a spectral resolution and local cloud information gives new insights on the atmospheric impact on solar energy.
The proper use of protective hoods on panel saws should reliably prevent severe injuries from (hand) contact with the blade or material kickbacks. It also should minimize long-term lung damages from fine-particle pollution. To achieve both purposes the hood must be adjusted properly by the operator for each workpiece to fit its height. After a work process is finished, the hood must be lowered down completely to the bench. Unfortunately, in practice the protective hood is fixed at a high position for most of the work time and herein loses its safety features. A system for an automatic height adjustment of the hood would increase comfort and safety. If the system can distinguish between workpieces and skin reliably, it furthermore will reduce occupational hazards for panel saw users. A functional demonstrator of such a system has been designed and implemented to show the feasibility of this approach. A specific optical sensor system is used to observe a point on the extended cut axis in front of the blade. The sensor determines the surface material reliably and measures the distance to the workpiece surface simultaneously. If the distance changes because of a workpiece fed to the machine, the control unit will set the motor-adjusted hood to the correct height. If the sensor detects skin, the hood will not be moved. In addition a camera observes the area under the hood. If there are no workpieces or offcuts left under the hood, it will be lowered back to the default position.
Semantic Image Segmentation Combining Visible and Near-Infrared Channels with Depth Information
(2015)
Image understanding is a vital task in computer vision that has many applications in areas such as robotics, surveillance and the automobile industry. An important precondition for image understanding is semantic image segmentation, i.e. the correct labeling of every image pixel with its corresponding object name or class. This thesis proposes a machine learning approach for semantic image segmentation that uses images from a multi-modal camera rig. It demonstrates that semantic segmentation can be improved by combining different image types as inputs to a convolutional neural network (CNN), when compared to a single-image approach. In this work a multi-channel near-infrared (NIR) image, an RGB image and a depth map are used. The detection of people is further improved by using a skin image that indicates the presence of human skin in the scene and is computed based on NIR information. It is also shown that segmentation accuracy can be enhanced by using a class voting method based on a superpixel pre-segmentation. Models are trained for 10-class, 3-class and binary classification tasks using an original dataset. Compared to the NIR-only approach, average class accuracy is increased by 7% for 10-class, and by 22% for 3-class classification, reaching a total of 48% and 70% accuracy, respectively. The binary classification task, which focuses on the detection of people, achieves a classification accuracy of 95% and true positive rate of 66%. The report at hand describes the proposed approach and the encountered challenges and shows that a CNN can successfully learn and combine features from multi-modal image sets and use them to predict scene labeling.
Persons entering the working range of industrial robots are exposed to a high risk of collision with moving parts of the system, potentially causing severe injuries. Conventional systems, which restrict the access to this area, range from walls and fences to light barriers and other vision based protective devices (VBPD). None of these systems allow to distinguish between humans and workpieces in a safe and reliable manner. In this work, a new approach is investigated, which uses an active near-infrared (NIR) camera system with advanced capabilities of skin detection to distinguish humans from workpieces based on characteristic spectral signatures. This approach allows to implement more intelligent muting processes and at the same time increases the safety of persons working close to the robots. The conceptual integration of such a camera system into a VBPD and the enhancement of person detection methods through skin detection are described and evaluated in this paper. Based upon this work, next steps could be the development of multimodal sensor systems to safeguard working ranges of collaborating robots using the described camera system.
This presentation gives an overview of current research in the area of high quality rendering and visualization at the Institute of Visual Computing (IVC). Our research facility has some unique software and hardware installations of which we will describe a large, ultra- high resolution (72 megapixel) video wall in this presentation.
In education, finding the appropriate learning pace that fits to the members of a large group is a challenging task. This becomes especially evident when teaching multidisciplinary subjects such as epidemiology in medicine or computer science in most study programs, since lecturers have to face a very heterogeneous state of previous knowledge. Approaching this issue requires an individual supervision of each and every student, which is obviously bounded by the available resources. Moreover, when referring back to the second example, writing computer programs requires a complex installation and configuration of development tools. Many beginning programmers already become stuck at this entry stage. This paper introduces WHELP, a Web-based Holistic E-Learning Platform, which provides an integrated environment enabling the learning and teaching of computer science topics without the need to install any software. Moreover, WHELP includes an interactive feedback system for each programming exercise, where lecturers or tutors can supply comments, improvements, code assistance or tips helping the students to accomplish their tasks. Furthermore, WHELP offers a statistical analysis module as well as a real-time classroom polling system both promoting an overview of the state of knowledge of a course. In addition to that, WHELP enables collaborative working including code-sharing and peer-to-peer learning. This feature enables students to work on exercises simultaneously at distinct places. WHELP has been successfully deployed in the winter term 2013 at the Cologne University of Applied Sciences supporting the 120 students and 3 lecturers to learn and teach basic topics of computer science in an engineering study program.