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Before gas is transported, natural gas traders have to plan with many contracts every day. If a cost-optimized solution is sought the most attractive contracts of a large contract set have to be selected. This kind of cost-optimization is also known as day-ahead balancing problem. In this work it is shown that it is possible to express this problem as a linear program that considers important influences and restrictions in the daily trading.
The aspects of the day-ahead balancing problem are examined and modelled individually. This way a basic linear program is gradually adapted towards a realistic mathematical formulation. The resulting linear optimization problem is implemented as a prototype that considers the discussed aspects of a cost-optimized contract selection.
The capabilities of Artificial Intelligence (AI) are utilized increasingly
in today‘s world. The autonomous and adaptive characteristics
allow applications to be more effective and efficient. A certain
subfield of Artificial Intelligence, Machine Learning, is enabling
services to be tailored to a user‘s specific needs. This could prove to
be useful in an information-heavy field such as Statistics. As design
research from SPSS Statistics, a legacy statistical application, has
indicated, statistics beginners struggle to tackle the challenge of
preparing a statistical research study. They turn to several sources
of information in an attempt to find help and answers but are not
always successful. This leads to them being unconfident before
they have even started to execute the statistical study. The adaptive
features of Artificial Intelligence could help support students
in this case, if designed according to established principles. This
thesis investigated the question whether an AI-powered solution
could elevate the users‘ confidence in statistical research studies.
In order to find the answer, a prototype with exemplary User Experience
was designed and implemented. Preceding research determined
the domain and market offer. User research was conducted
to ensure a human-centered outcome. The prototype was evaluated
with real test users and the results answered the question in
the affirmative.
Today’s digital cameras use a mosaic of red, green, and blue color filters to capture images in three color channels on a single sensor plane. This thesis investigates the use of convolutional neural networks (CNNs) for demosaicing – the process of reconstructing full-color images from raw mosaic sensor data. While there are existing CNNs for demosaicing raw images from the well-established regular Bayer color filter array (CFA), this thesis focuses on how they perform on alternative non-regular sampling patterns that produce less aliasing artifacts, namely the stochastic Gaussian- and the RandomQuarter sampling pattern (Backes and Fröhlich, 2020).
A basic UNet (Ronneberger et al., 2015) and the spatially adaptive SANet (T. Zhang et al., 2022) are implemented in a supervised training pipeline based on the PixelShift200 image dataset (Qian et al., 2021) to investigate their suitability for the irregular demosaicing task. The experiments indicate that the basic UNet encounters difficulties in restoring the missing color values, whereas the spatially adaptive convolutional layers help in processing the irregularly sampled raw images.
In addition, this thesis enhances SANet effectiveness by employing an alternative residual branch based on a CFA-normalized Gaussian filter, as well as a tileable modification to the Gaussian CFA pattern. The modified SANet is shown to outperform the conventional dFSR algorithm (Backes & Fröhlich, 2020) in terms of peak signal to noise ratio (PSNR) and structural similarity index measure (SSIM).
Privacy in Social Networks
(2016)
Online Social Networks (OSNs) are heavily used today and despite of all privacy concerns found a way into our daily life. After showing how heavy data collection is a violation of the user's privacy, this thesis establishes mandatory and optional requirements for a Privacy orientated Online Social Network (POSN). It evaluates twelve existing POSNs in general and in regard to those requirements. The paper will find that none of these POSNs are able to fulfill the requirements and therefore proposes features and patterns as a reference architecture.
By now GPUs have become powerful general purpose processors that found their way not only into desktop systems but also supercomputers. To use GPUs efficiently one needs to understand their basic architecture and their limitations. We take a look at how GPUs evolved and how they differ from CPUs to gain a deeper understanding of the workloads well suited for GPUs.
The increasing availability of online video content, partially fueled by the Covid-19 pandemic and the growing presence of social media, adds to the importance of providing audio descriptions as a media alternative to video content for blind and visually impaired people. In order to address concerns as to what can be sufficiently described and how such descriptions can be delivered to users, a concept has been developed providing audio descriptions in multiple levels of detail. Relevant information is incorporated into an XML-based data structure. The concept also includes a process to provide optional explanations to terms and abbreviations, helping users without specific knowledge or people with cognitive concerns in comprehending complex videos. These features are implemented into a prototype based on the Able Player software. By conducting a user test, the benefits of multi-layered audio descriptions and optional explanatory content are evaluated. Findings suggest that the choice of several levels of detail is received positively. Users acknowledged the concept of explanations played parallelly to the video and described further use cases for such a practice. Participants preferred a higher level of detail for a high-paced action video and a lower level for informative content. Possibilities to extend the data structure and features include multilanguage use cases and distributed systems.
Deep learning methods have proven highly effective for object recognition tasks, especially
in the form of artificial neural networks. In this bachelor’s thesis, a way is shown to imple-
ment a ready-to-use object recognition implementation on the NAO robotic platform using
Convolutional Neural Networks based on pretrained models. Recognition of multiple objects
at once is realized with the help of the Multibox algorithm. The implementation’s object
recognition rates are evaluated and analyzed in several tests.
Furthermore, the implementation offers a graphical user interface with several options to
adjust the recognition process and for controlling movements of the robot’s head in order
to easier acquire objects in the field of view. Additionally, a dialogue system for querying
further results is presented.
Massively Multiplayer Online Games (MMOGs) are increasing in both popularity and scale.
One of the reasons for this is that interacting with human counterparts is typically considered much more interesting than playing against an Artificial Intelligence.
Although the visual quality of game worlds has increased over the past years,they often fall short in providing consistency with regard to behavior and interactivity.
This is especially true for the game worlds of MMOGs. One way of making a game world feel more alive is to implement a Fire Propagation System that defines show fire spreads in the game world. Singleplayer games like Far Cry 2 and The Legend of Zelda:
Breath of the Wild already feature implementations of such a system. As far as the author of this thesis knows, however, noMMOGwith an implemented Fire Propagation System has been released yet. This work introduces two approaches for developing such a system for a MMOG with a client-server architecture.
It was implemented using the proprietary game engine Snowdrop. The approaches presented in this thesis can be used as a basis for developing a Fire Propagation System and can be adjusted easily to fit the needs of a specific project.
Virtual-reality (VR) is an immersive technology with a growing market and many applications for gesture recognition. This thesis presents a VR gesture recognition method using signal processing techniques. The core concept is based on the comparison of motion features in the form of signals between a runtime recording of users and a possible gesture set. This comparison yields a similarity score through which the most similar gesture can be recognized by a continuous recognition system. Some selected comparison methods are presented, evaluated and discussed. An example implementation is demonstrated. However, due to an introduced layer model parts of the method and its implementation are interchangeable.
Similar or even better performance is achieved compared to other related work. The comparison method Dynamic Time Warping (DTW) reaches an average positive recognitions rate of 98.18% with acceptable real-time application performance. Additionally, the method comes with some benefits: position and direction of users is irrelevant, body proportions have no significant negative impact on recognition rates, faster and slower gesture executions are possible, no user inputs are needed to communicate gesture start and end (continuous recognition), also continuous gestures can be recognized, and the recognition is fast enough to trigger gesture specific events already during the execution.
Multiplayer games can increase player enjoyment through social interactions, cooperation, and competition. Their market popularity shows the success of especially networked multiplayer games, which pose new networking challenges to game developers. The main challenge is synchronizing game state across players. Research identifies deterministic lockstep, snapshot interpolation, and state-sync as primary methods for this task, each with distinct advantages and disadvantages.
This work, and the master thesis this paper is based on, quantitatively evaluated deterministic lockstep, demonstrating its vertical (entity count) and horizontal (player count) scaling limitations and compares the method to snapshot interpolation. Lockstep supports minimum 16,000 entities for up to 10 players and a horizontal scaling of 40 or more players with 1024 entities. However, a negative correlation between entity and player count limits was observed, which was indicated by the maximum scaling configurations 30 players with 4096 entities or 20 players with 8192 entities. Snapshot interpolation faced a vertical limit with 4096 entities and 10 players and horizontally with 40 or more players and 1024 entities.
The paper further contributes by comparing results to related work, summarizing synchronization methods, proposing a hybrid architecture model of deterministic lockstep with snapshot interpolation for re-synchronization and hot-joins, and deconstructing Unity Transport Package’s (UTP) network packets.