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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.
Video games have a significant influence on our time. However, lack of accessibility makes it hard for disabled gamers to play most of them. Virtual reality offers new possibilities to include people with disabilities and enable them to play games. Additionally, serious VR games provide educational benefits, such as improved memory and engagement.
In this work, the accessibility problems in video games and VR applications are explored with an emphasis on serious games as well as a general lack of guidelines. An overview of existing guidelines is given. From this, a set of guidelines is derived that summarizes the relevant rules for accessible VR games.
New ways to interact with VR environments come with both opportunities and challenges. This work investigates the applicability of different hands-free input methods to play a VR game. Using a serious game five focus and three activation methods were implemented exemplary with the Oculus Go. The suitability of these methods was analyzed in a pre-study that excluded head movements for controlling the game. The remaining input methods were evaluated in an explorative user study in terms of operability and ease of use.In summary, all tested methods can be used to control the game. The evaluation shows head-tracking as the preferred input method, while scanning eye-tracking and voice control were rated mediocre.
In addition, the correlation between input methods and different menu types was examined, but the influence turned out to be negligible.
Talking about highly scalable and reliable sys-
tems, issues like logging and monitoring are often
disregarded. However, being able to manage to-
day’s software systems absolutely requires deep
knowledge about the current state of applications
as well as the underlying infrastructure. Extract-
ing and preparing debug information as well as
various metrics in a fast and clearly arranged
manner is an essential precondition in order to
handle this task.
Since we at Bertsch Innovation GmbH also
face increasing requirements concerning Media-
Cockpit as one of our core products, we decided
to establish a centralized logging infrastructure
in order to come up to the application’s evolution
towards a more and more distributed system.
In this paper, I want to describe the steps
that I have taken in order to setup a functioning
logging tool stack consisting of Elasticsearch,
Logstash and Kibana (usually abbreviated as ELK stack ). Besides outlining proper
setup and configuration, I will also discuss possi-
ble pitfalls as well as custom adjustments made
when ELK did not meet our demands.
Nowadays more and more companies use agile software development to build software in short release cycles. Monolithic applications are split into microservices, which can independently be maintained and deployed by agile teams. Modern platforms like Docker support this process. Docker offers services to containerize such services and orchestrate them in a container cluster. A software supply chain is the umbrella term for the process of developing, automated building and testing, as well as deploying a complete application. By combining a software supply chain and Docker, those processes can be automated in standardized environments. Since Docker is a young technology and software supply chains are critical processes in organizations, security needs to be reviewed. In this work a software supply chain based on Docker is built and a threat modeling process is used to assess its security. The main components are modeled and threats are identified using STRIDE. Afterwards risks are calculated and methods to secure the software supply chain based on security objectives confidentiality, integrity and availability are discussed. As a result, some components require special treatments in security context since they have a high residual risk of being targeted by an attacker. This work can be used as basis to build and secure the main components of a software supply chain. However additional components such as logging, monitoring as well as integration into existing business processes need to be reviewed.
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.
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.
Multiplayer games can increase player enjoyment through social interactions, cooperation and competition. The popularity of such games is shown by current market trends. Especially networked multiplayer games frequently achieve great success, but confront game developers with additional networking challenges in the already complex field of game production. The primary challenge is game state synchronization across all players. Based on the current research, there are three main methods for this task – deterministic lockstep, snapshot interpolation and state-sync – with their own advantages and disadvantages.
This work quantitatively evaluated and discussed the vertical (entity count) and horizontal (player count) limitations of deterministic lockstep and compared the method to snapshot interpolation. Results showed, that deterministic lockstep has no indicated vertical scaling limitation with a player count of up to 10 supporting 16,000 or more entities. A horizontal scaling limitation could not be found either and lockstep was confirmed to work with 40 or more players while handling 1024 entities. However, both scaling dimensions correlate negatively, which was indicated by the maximum scaling configurations 30 players and 4096 entities or 20 players and 8192 entities.
An unoptimized snapshot interpolation implementation achieved a vertical scaling limitation of 4096 entities with 10 players and a horizontal scaling limit of 40 or more players with 1024 entities and therefore was found to have a lower entity limit compared to deterministic lockstep.
Furthermore, results are compared to related work. Other contributions of this thesis include an overview of game networks and the three game state synchronization techniques. An architecture model for deterministic lockstep including a hybrid approach combining it with snapshot interpolation for re-synchronization and hot-joins. And finally, a network packet deconstruction of the implemented networking framework Unity Transport Package (UTP).
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.
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.
In recent years new trends such as industry 4.0 boosted the research and
development in the field of autonomous systems and robotics. Robots collaborate and
even take over complete tasks of humans. But the high degree of automation requires
high reliability even in complex and changing environments. Those challenging
conditions make it hard to rely on static models of the real world. In addition to
adaptable maps, mobile robots require a local and current understanding of the scene.
The Bosch Start-Up Company is developing robots for intra-logistic systems, which
could highly benefit from such a detailed scene understanding. The aim of this work
is to research and develop such a system for warehouse environments. While the
possible field of application is in general very broad, this work will focus on the
detection and localization of warehouse specific objects such as palettes.
In order to provide a meaningful perception of the surrounding a RGB-D camera is
used. A pre-trained convolutional network extracts scene understanding in the form
of pixelwise class labels. As this convolutional network is the core of the application,
this work focuses on different network set-ups and learning strategies. One difficulty
was the lack of annotated training data. Since the creation of densely labeled images
is a very time consuming process it was important to elaborate on good alternatives.
One interesting finding was that it’s possible to transfer learning to a high extent from
similar models pre-trained on thousands of RGB-images. This is done by selective
interventions on the net parameters. By ensuring a good initialization it’s possible
to train towards a well performing model within few iterations. In this way it’s
possible to train even branched nets at once. This can also be achieved by including
certain normalization steps. Another important aspect was to find a suitable way
to incorporate depth-information. How to fuse depth into the existing model? By
providing the height over ground as an additional feature the segmentation accuracy
was further improved while keeping the extra computational costs low.
Finally the segmentation maps are refined by a conditional random field. The joint
training of both parts results in accurate object segmentations comparable to recently
published state-of-the-art models.