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Synthesis of serving policies for objects flow in the system with refillable storage component
(2017)
Current robot platforms are being employed to collaborate with humans in a wide range of domestic and industrial tasks. These environments require autonomous systems that are able to classify and communicate anomalous situations such as fires, injured persons, car accidents; or generally, any potentially dangerous situation for humans. In this paper we introduce an anomaly detection dataset for the purpose of robot applications as well as the design and implementation of a deep learning architecture that classifies and describes dangerous situations using only a single image as input. We report a classification accuracy of 97 % and METEOR score of 16.2. We will make the dataset publicly available after this paper is accepted.
Today, more than 70 million tons of lignin are produced by the pulp and paper industry every year. However, the utilization of lignin as a source for chemical synthesis is still limited due to the complex and heterogeneous lignin structure. The purpose of this study was a selective photodegradation of industrially available kraft lignin in order to obtain appropriate fragments and building block chemicals for further utilization, e.g. polymerization. Thus, kraft lignin obtained from soft wood black liquor by acidification was dissolved in sodium hydroxide and irradiated at a wavelength of 254 nm with and without the presence of titanium dioxide in various concentrations. Analyses of the irradiated products via SEC showed decreasing molar masses and decreasing polydispersity indices over time. At the end of the irradiation period the lignin was depolymerised to form fragments as small as the lignin monomers. TOC analyses showed minimal mineralisation due to the depolymerisation process.
This work presents the analysis of data recorded by an eye tracking device in the course of evaluating a foveated rendering approach for head-mounted displays (HMDs). Foveated rendering methods adapt the image synthesis process to the user’s gaze and exploiting the human visual system’s limitations to increase rendering performance. Especially, foveated rendering has great potential when certain requirements have to be fulfilled, like low-latency rendering to cope with high display refresh rates. This is crucial for virtual reality (VR), as a high level of immersion, which can only be achieved with high rendering performance and also helps to reduce nausea, is an important factor in this field. We put things in context by first providing basic information about our rendering system, followed by a description of the user study and the collected data. This data stems from fixation tasks that subjects had to perform while being shown fly-through sequences of virtual scenes on an HMD. These fixation tasks consisted of a combination of various scenes and fixation modes. Besides static fixation targets, moving tar- gets on randomized paths as well as a free focus mode were tested. Using this data, we estimate the precision of the utilized eye tracker and analyze the participants’ accuracy in focusing the displayed fixation targets. Here, we also take a look at eccentricity-dependent quality ratings. Comparing this information with the users’ quality ratings given for the displayed sequences then reveals an interesting connection between fixation modes, fixation accuracy and quality ratings.
Management der Rehabilitation: Case Management im Handlungsfeld Rehabilitation. Band 1 - Grundlagen
(2017)
Mit dem Paradigmenwechsel im Verständnis von Rehabilitation, weg von der rein defizitorientierten, medizinischen Sichtweise hin zur selbstbestimmten Teilhabe am Leben in der Gesellschaft, der mit dem Inkrafttreten des SGB IX im Jahr 2001 in Deutschland, der Ratifizierung der UN-Behindertenrechtskonvention im Jahr 2009, der Weiterentwicklung des Behindertengleichstellungsrechts und der Verabschiedung des Bundesteilhabegesetztes im Jahr 2016 endgültig vollzogen wurde, haben sich die Anforderungen an die Strukturen und Prozesse derjenigen Institutionen verändert, die mit der Organisation, Durchführung und Finanzierung von Rehabilitation befasst sind. Rehabilitation entwickelt sich damit von einer nachgelagerten (Teil-)Leistung zu einer der Schlüsselstrategien für die gesundheitliche Versorgung und soziale Sicherung.
The Sparse Matrix Vector Multiplication is an important operation on sparse matrices. This operation is the most time consuming operation in iterative solvers and therefore an efficient execution of that operation is of great importance for many applications. Numerous different storage formats that store sparse matrices efficiently have already been established. Often, these storage formats utilize the sparsity pattern of a matrix in an appropiate manner. For one class of sparse matrices the nonzero values occur in small dense blocks and appropriate block storage formats are well suited for such patterns. But on the other side, these formats perform often poor on general matrices without an explicit / regular block structure. In this paper, the newly developed sparse matrix format DynB is introduced. The aim is to efficiently use several optimization approaches and vectorization with current processors, even for matrices without an explicit block structure of nonzero elements. The DynB matrix format uses 2D rectangular blocks of variable size, allowing fill-ins per block of explicit zero values up to a user controllable threshold. We give a simple and fast heuristic to detect such 2D blocks in a sparse matrix. The performance of the Sparse Matrix Vector Multiplication for a selection of different block formats and matrices with different sparsity structures is compared. Results show that the benefit of blocking formats depend – as to be expected – on the structure of the matrix and that variable sized block formats like DynB can have advantages over fixed size formats and deliver good performance results even for general sparse matrices.
In this paper we propose an implement a general convolutional neural network (CNN) building framework for designing real-time CNNs. We validate our models by creating a real-time vision system which accomplishes the tasks of face detection, gender classification and emotion classification simultaneously in one blended step using our proposed CNN architecture. After presenting the details of the training procedure setup we proceed to evaluate on standard benchmark sets. We report accuracies of 96% in the IMDB gender dataset and 66% in the FER-2013 emotion dataset. Along with this we also introduced the very recent real-time enabled guided back-propagation visualization technique. Guided back-propagation uncovers the dynamics of the weight changes and evaluates the learned features. We argue that the careful implementation of modern CNN architectures, the use of the current regularization methods and the visualization of previously hidden features are necessary in order to reduce the gap between slow performances and real-time architectures. Our system has been validated by its deployment on a Care-O-bot 3 robot used during RoboCup@Home competitions. All our code, demos and pre-trained architectures have been released under an open-source license in our public repository.