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Error analysis in a high accuracy sampled-data velocity stabilising system using Volterra series
(2015)
We present GEM-NI -- a graph-based generative-design tool that supports parallel exploration of alternative designs. Producing alternatives is a key feature of creative work, yet it is not strongly supported in most extant tools. GEM-NI enables various forms of exploration with alternatives such as parallel editing, recalling history, branching, merging, comparing, and Cartesian products of and for alternatives. Further, GEM-NI provides a modal graphical user interface and a design gallery, which both allow designers to control and manage their design exploration. We conducted an exploratory user study followed by in-depth one-on-one interviews with moderately and highly skills participants and obtained positive feedback for the system features, showing that GEM-NI supports creative design work well.
We present a system that combines voxel and polygonal representations into a single octree acceleration structure that can be used for ray tracing. Voxels are well-suited to create good level-of-detail for high-frequency models where polygonal simplifications usually fail due to the complex structure of the model. However, polygonal descriptions provide the higher visual fidelity. In addition, voxel representations often oversample the geometric domain especially for large triangles, whereas a few polygons can be tested for intersection more quickly.
Detection of triacetone triperoxide using temperature cycled metal-oxide semiconductor gas sensors
(2015)
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.
This paper proposes a new artificial neural network-based maximum power point tracker for photovoltaic application. This tracker significantly improves efficiency of the photovoltaic system with series-connection of photovoltaic modules in non-uniform irradiance on photovoltaic array surfaces. The artificial neural network uses irradiance and temperature sensors to generate the maximum power point reference voltage and employ a classical perturb and observe searching algorithm. The structure of the artificial neural network was obtained by numerical modelling using Matlab/Simulink. The artificial neural network was trained using Bayesian regularisation back-propagation algorithms and demonstrated a good prediction of the maximum power point. Relative number of Vmpp prediction errors in range of ±0.2V is 0.05% based on validation data.
IT-accessiblity is often treated as an orphan in companies. Even though the proportion of disabled people is substantial and people become older and more susceptible to disabilities. Besides cost factors, companies often do not have a plan how to implement and control IT-accessibility successfully. However, most companies are familiar with IT-maturity frameworks to evaluate and improve their own IT-infrastructure. It would facilitate dealing with IT-accessibility, if IT-maturity frameworks consider IT-accessibility and provide recommendations and solutions for a successful implementation. Therefore, this article conducts a review of an acknowledged IT-maturity framework with regard to its capability to enable implementation of IT-accessibility in an organization. The first part of this article will illustrate the motivation and background for the authors concern with such a topic. Afterwards the authors will introduce the reader to the reviewed IT-maturity framework and provide basic knowledge on IT-accessibility. The main part of the article will deal with the review of the applied IT-maturity framework and outline examples of critical capabilities for successfully implementing IT-accessibility in an organization. The final section will derive implications and close with planned future research activities in this field.
Secure vehicular communication has been discussed over a long period of time. Now,- this technology is implemented in different Intelligent Transportation System (ITS) projects in europe. In most of these projects a suitable Public Key Infrastructure (PKI) for a secure communication between involved entities in a Vehicular Ad hoc Network (VANET) is needed. A first proposal for a PKI architecture for Intelligent Vehicular Systems (IVS PKI) is given by the car2car communication consortium. This architecture however mainly deals with inter vehicular communication and is less focused on the needs of Road Side Units. Here, we propose a multi-domain PKI architecture for Intelligent Transportation Systems, which considers the necessities of road infrastructure authorities and vehicle manufacturers, today. The PKI domains are cryptographically linked based on local trust lists. In addition, a crypto agility concept is suggested, which takes adaptation of key length and cryptographic algorithms during PKI operation into account.
The latest advances in the field of smart card technologies allow modern cards to be more than just simple security tokens. Recent developments facilitate the use of interactive components like buttons, displays or even touch-sensors within the card's body thus conquering whole new areas of application. With interactive functionalities the usability aspect becomes the most important one for designing secure and popularly accepted products. Unfortunately, the usability can only be tested fully with completely integrated hence expensive smart card prototypes. This restricts severely application specific research, case studies of new smart card user interfaces and the optimization of design aspects, as well as hardware requirements by making usability and acceptance tests in smart card development very costly and time-consuming. Rapid development and simulation of smart card interfaces and applications can help to avoid this restriction. This paper presents a rapid development process for new smart card interfaces and applications based on common smartphone technology using a tool called SCUID^Sim. We will demonstrate the variety of usability aspects that can be analyzed with such a simulator by discussing some selected example projects.
This paper proposes an Artificial Plasmodium Algorithm (APA) mimicked a contraction wave of a plasmodium of physarum polucephalum. Plasmodia can live using the contracion wave in their body to communicate to others and transport a nutriments. In the APA, each plasmodium has two information as the wave information: the direction and food index. We apply the APA to 4 types of mazes and confirm that the APA can solve the mazes.
This paper proposes an Artificial Plasmodium Algorithm (APA) mimicked a contraction wave of a plasmodium of physarum polucephalum. Plasmodia can live using the contracion wave in their body to communicate to others and transport a nutriments. In the APA, each plasmodium has two information as the wave information: the direction and food index. We apply APA to a maze solving and route planning of road map.
Simultaneous multifrequency radio observations of the Galactic Centre magnetar SGR J1745-2900
(2015)
We propose a high-performance GPU implementation of Ray Histogram Fusion (RHF), a denoising method for stochastic global illumination rendering. Based on the CPU implementation of the original algorithm, we present a naive GPU implementation and the necessary optimization steps. Eventually, we show that our optimizations increase the performance of RHF by two orders of magnitude when compared to the original CPU implementation and one order of magnitude compared to the naive GPU implementation. We show how the quality for identical rendering times relates to unfiltered path tracing and how much time is needed to achieve identical quality when compared to an unfiltered path traced result. Finally, we summarize our work and describe possible future applications and research based on this.
In this doctoral thesis the curing process of visible light-curing (VLC) dental composites and 3D printing rapid prototyping (RP) materials are investigated with the focus on dielectric analysis (DEA). This method is able to monitor the curing of resins in an alternating electric fringe field with adjustable frequencies and is often used for cure control of composites manufacturing in the aviation and automotive industry but hardly established in dental science or RP method development. It is capable of investigating very fast initiation and primary curing processes using high frequencies in the kHz-range. The aim of the Thesis is a better understanding of the curing processes with respect to curing parameters such as resin composition, viscosity, temperature, and for light-curing composites also light intensity and irradiation depth. Due to the nature of both dental and RP systems an application of specific experimental set-up had to be designed allowing for the generation of reproducible and valid results. Subsequently, different evaluation methods were developed to characterize the curing behavior of both material types. A special focus was paid to the determination of kinetic parameters from DEA measurements. Reaction rates of the curing of the corresponding thermosets were calculated and applied to the ion viscosity curves measured by DEA to evaluate reaction kinetic parameters. For the dental composites it could be clearly shown that the initial curing rate is directly proportional to light intensity and not to its square root as proposed by many others authors. A good description of the curing behaviour of 3DP RP materials was also achieved assuming a reaction order smaller than one. This data provides the base for the kinetic modeling of polymerization and curing processes proposed within the Thesis.
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.
Over the last 50 years, the controlled motion of robots has become a very mature domain of expertise. It can deal with all sorts of topologies and types of joints and actuators, with kinematic as well as dynamic models of devices, and with one or several tools or sensors attached to the mechanical structure. Nevertheless, the domain has not succeeded in standardizing the modelling of robot devices (including such fundamental entities as “reference frames”!), let alone the semantics of their motion specification and control. This thesis aims to solve this long-standing problem, from three different sides: semantic models for robot kinematics and dynamics, semantic models of all possible motion specification and control problems, and software that can support the latter while being configured by a systematic use of the former.