54 Informatik
Refine
Document Type
- Article (2)
- Doctoral Thesis (1)
Institute
Keywords
- Maschinelles Lernen (2)
- 3D Telepresence (1)
- Camera Calibration (1)
- Depth Camera (1)
- Funktechnik (1)
- Internet of Things (1)
- Machine learning (1)
- Mensch-Maschine-Kommunikation (1)
- OA-Publikationsfonds2019 (1)
- OA-Publikationsfonds2020 (1)
Year of publication
- 2019 (3) (remove)
FCS-MBFLEACH: Designing an Energy-Aware Fault Detection System for Mobile Wireless Sensor Networks
(2019)
Wireless sensor networks (WSNs) include large-scale sensor nodes that are densely distributed over a geographical region that is completely randomized for monitoring, identifying, and analyzing physical events. The crucial challenge in wireless sensor networks is the very high dependence of the sensor nodes on limited battery power to exchange information wirelessly as well as the non-rechargeable battery of the wireless sensor nodes, which makes the management and monitoring of these nodes in terms of abnormal changes very difficult. These anomalies appear under faults, including hardware, software, anomalies, and attacks by raiders, all of which affect the comprehensiveness of the data collected by wireless sensor networks. Hence, a crucial contraption should be taken to detect the early faults in the network, despite the limitations of the sensor nodes. Machine learning methods include solutions that can be used to detect the sensor node faults in the network. The purpose of this study is to use several classification methods to compute the fault detection accuracy with different densities under two scenarios in regions of interest such as MB-FLEACH, one-class support vector machine (SVM), fuzzy one-class, or a combination of SVM and FCS-MBFLEACH methods. It should be noted that in the study so far, no super cluster head (SCH) selection has been performed to detect node faults in the network. The simulation outcomes demonstrate that the FCS-MBFLEACH method has the best performance in terms of the accuracy of fault detection, false-positive rate (FPR), average remaining energy, and network lifetime compared to other classification methods.
Modern immersive telepresence systems enable people at different locations to meet in virtual environments using realistic three-dimensional representations of their bodies. For the realization of such a three-dimensional version of a video conferencing system, each user is continuously recorded in 3D. These 3D recordings are exchanged over the network between remote sites. At each site, the remote recordings of the users, referred to as 3D video avatars, are seamlessly integrated into a shared virtual scenery and displayed in stereoscopic 3D for each user from his or her perspective.
This thesis reports on algorithmic and technical contributions to modern immersive telepresence systems and presents the design, implementation and evaluation of the first immersive group-to-group telepresence system in which each user is represented as realistic life-size 3D video avatar. The system enabled two remote user groups to meet and collaborate in a consistent shared virtual environment. The system relied on novel methods for the precise calibration and registration of color- and depth- sensors (RGBD) into the coordinate system of the application as well as an advanced distributed processing pipeline that reconstructs realistic 3D video avatars in real-time. During the course of this thesis, the calibration of 3D capturing systems was greatly improved. While the first development focused on precisely calibrating individual RGBD-sensors, the second stage presents a new method for calibrating and registering multiple color and depth sensors at a very high precision throughout a large 3D capturing volume. This method was further refined by a novel automatic optimization process that significantly speeds up the manual operation and yields similarly high accuracy. A core benefit of the new calibration method is its high runtime efficiency by directly mapping from raw depth sensor measurements into an application coordinate system and to the coordinates of its associated color sensor. As a result, the calibration method is an efficient solution in terms of precision and applicability in virtual reality and immersive telepresence applications. In addition to the core contributions, the results of two case studies which address 3D reconstruction and data streaming lead to the final conclusion of this thesis and to directions of future work in the rapidly advancing field of immersive telepresence research.
Estimating the solubility of carbon dioxide in ionic liquids, using reliable models, is of paramount importance from both environmental and economic points of view. In this regard, the current research aims at evaluating the performance of two data-driven techniques, namely multilayer perceptron (MLP) and gene expression programming (GEP), for predicting the solubility of carbon dioxide (CO2) in ionic liquids (ILs) as the function of pressure, temperature, and four thermodynamical parameters of the ionic liquid. To develop the above techniques, 744 experimental data points derived from the literature including 13 ILs were used (80% of the points for training and 20% for validation). Two backpropagation-based methods, namely Levenberg–Marquardt (LM) and Bayesian Regularization (BR), were applied to optimize the MLP algorithm. Various statistical and graphical assessments were applied to check the credibility of the developed techniques. The results were then compared with those calculated using Peng–Robinson (PR) or Soave–Redlich–Kwong (SRK) equations of state (EoS). The highest coefficient of determination (R2 = 0.9965) and the lowest root mean square error (RMSE = 0.0116) were recorded for the MLP-LMA model on the full dataset (with a negligible difference to the MLP-BR model). The comparison of results from this model with the vastly applied thermodynamic equation of state models revealed slightly better performance, but the EoS approaches also performed well with R2 from 0.984 up to 0.996. Lastly, the newly established correlation based on the GEP model exhibited very satisfactory results with overall values of R2 = 0.9896 and RMSE = 0.0201.