Efficient and robust semantic mapping for indoor environments. - In: 2022 IEEE International Conference on Robotics and Automation (ICRA), (2022), S. 9221-9227
A key proficiency an autonomous mobile robot must have to perform high-level tasks is a strong understanding of its environment. This involves information about what types of objects are present, where they are, what their spatial extend is, and how they can be reached, i.e., information about free space is also crucial. Semantic maps are a powerful instrument providing such information. However, applying semantic segmentation and building 3D maps with high spatial resolution is challenging given limited resources on mobile robots. In this paper, we incorporate semantic information into efficient occupancy normal distribution transform (NDT) maps to enable real-time semantic mapping on mobile robots. On the publicly available dataset Hypersim, we show that, due to their sub-voxel accuracy, semantic NDT maps are superior to other approaches. We compare them to the recent state-of-the-art approach based on voxels and semantic Bayesian spatial kernel inference (S-BKI) and to an optimized version of it derived in this paper. The proposed semantic NDT maps can represent semantics to the same level of detail, while mapping is 2.7 to 17.5 times faster. For the same grid resolution, they perform significantly better, while mapping is up to more than 5 times faster. Finally, we prove the real-world applicability of semantic NDT maps with qualitative results in a domestic application.
https://doi.org/10.1109/ICRA46639.2022.9812205
State estimation for coupled reaction-diffusion PDE systems using modulating functions. - In: Sensors, ISSN 1424-8220, Bd. 22 (2022), 13, 5008, S. 1-24
Many systems with distributed dynamics are described by partial differential equations (PDEs). Coupled reaction-diffusion equations are a particular type of these systems. The measurement of the state over the entire spatial domain is usually required for their control. However, it is often impossible to obtain full state information with physical sensors only. For this problem, observers are developed to estimate the state based on boundary measurements. The method presented applies the so-called modulating function method, relying on an orthonormal function basis representation. Auxiliary systems are generated from the original system by applying modulating functions and formulating annihilation conditions. It is extended by a decoupling matrix step. The calculated kernels are utilized for modulating the input and output signals over a receding time window to obtain the coefficients for the basis expansion for the desired state estimation. The developed algorithm and its real-time functionality are verified via simulation of an example system related to the dynamics of chemical tubular reactors and compared to the conventional backstepping observer. The method achieves a successful state reconstruction of the system while mitigating white noise induced by the sensor. Ultimately, the modulating function approach represents a solution for the distributed state estimation problem without solving a PDE online.
https://doi.org/10.3390/s22135008
Magnetic hybrid materials interact with biological matrices. - In: Magnetic hybrid-materials, (2022), S. 681-738
Magnetic hybrid materials are a promising group of substances. Their interaction with matrices is challenging with regard to the underlying physical and chemical mechanisms. But thinking matrices as biological membranes or even structured cell layers they become interesting with regard to potential biomedical applications. Therefore, we established in vitro blood-organ barrier models to study the interaction and processing of superparamagnetic iron oxide nanoparticles (SPIONs) with these cellular structures in the presence of a magnetic field gradient. A one-cell-type-based blood-brain barrier model was used to investigate the attachment and uptake mechanisms of differentially charged magnetic hybrid materials. Inhibition of clathrin-dependent endocytosis and F-actin depolymerization led to a dramatic reduction of cellular uptake. Furthermore, the subsequent transportation of SPIONs through the barrier and the ability to detect these particles was of interest. Negatively charged SPIONs could be detected behind the barrier as well as in a reporter cell line. These observations could be confirmed with a two-cell-type-based blood-placenta barrier model. While positively charged SPIONs heavily interact with the apical cell layer, neutrally charged SPIONs showed a retarded interaction behavior. Behind the blood-placenta barrier, negatively charged SPIONs could be clearly detected. Finally, the transfer of the in vitro blood-placenta model in a microfluidic biochip allows the integration of shear stress into the system. Even without particle accumulation in a magnetic field gradient, the negatively charged SPIONs were detectable behind the barrier. In conclusion, in vitro blood-organ barrier models allow the broad investigation of magnetic hybrid materials with regard to biocompatibility, cell interaction, and transfer through cell layers on their way to biomedical application.
Automatic detection and prediction of discontinuities in laser beam butt welding utilizing deep learning. - In: Journal of advanced joining processes, ISSN 2666-3309, Bd. 6 (2022), 100119, S. 1-11
Laser beam butt welding of thin sheets of high-alloy steel can be really challenging due to the formation of joint gaps, affecting weld seam quality. Industrial approaches rely on massive clamping systems to limit joint gap formation. However, those systems have to be adapted for each individually component geometry, making them very cost-intensive and leading to a limited flexibility. In contrast, jigless welding can be a high flexible alternative to substitute conventionally used clamping systems. Based on the collaboration of different actuators, motions systems or robots, the approach allows an almost free workpiece positioning. As a result, jigless welding gives the possibility for influencing the formation of the joint gap by realizing an active position control. However, the realization of an active position control requires an early and reliable error prediction to counteract the formation of joint gaps during laser beam welding. This paper proposes different approaches to predict the formation of joint gaps and gap induced weld discontinuities in terms of lack of fusion based on optical and tactile sensor data. Our approach achieves 97.4 % accuracy for video-based weld discontinuity detection and a mean absolute error of 0.02 mm to predict the formation of joint gaps based on tactile length measurements by using inductive probes.
https://doi.org/10.1016/j.jajp.2022.100119
A data driven coordination between load balancing and interference cancellation. - In: Network and service management in the era of cloudification, softwarization and artificial intelligence, (2022), insges. 6 S.
Having multiple optimization functions in a mobile network brings demanding challenges in terms of coordination of potentially conflicting objectives. Typically each function aims at optimizing specific utilities modifying parameters that are coupled to other functions, which jeopardizes the stability of the system, specially if the policies followed by each function are dissonant. There are two commonly accepted approaches for Self-organized networks Function (SF) coordination: on the one hand side, there are heading (tailing) orchestration techniques in which a priori (posteriori) individual parameter conciliation takes place. On the other hand, it is possible address the conflict among SFs using global algorithms and optimize the network performance as a whole in a centralized manner. In this study, we aim at a solution of the second kind, interleaving the dynamic of two well-known SFs, namely Mobility Load Balancing (MLB) and Inter-Cell Interference Cancellation (ICIC), into one global technique and optimizing the global network performance using a method that combines fixed point algorithms and machine learning.
https://doi.org/10.1109/NOMS54207.2022.9789773
Decentralized machine learning based network data analytics for cognitive management of mobile communication networks. - In: Network and service management in the era of cloudification, softwarization and artificial intelligence, (2022), insges. 9 S.
The importance of network data analytics using advanced Machine Learning (ML) algorithms has been very well realized by the Telco industry and has resulted in the introduction of a dedicated Network Data Analytics Function (NWDAF) in the 5G service-based architecture in order to address the issues of integrating analytics into the network. The standardization of NWDAF by the 3rd Generation Partnership Project (3GPP) would enable third-party data analytics service providers to develop and provide AI-driven data analytics services to the Mobile Network Operators. The next-generation Radio Access Networks would require advanced analytics to drive closed-loop self-organizing network functions that are targeted to cognitively enhance network ef ciency and reduce the operational and capital costs of network operators. The existing solutions in this domain rely on conventional ML approaches that require the training data to be accumulated on a single data center. The concerns in this area would be the network overload and the privacy of the network operators that are sharing huge volumes of sensitive network data to the third-party Network Data Analytics Services (NDAS) executing over edge cloud infrastructures, perhaps even operated by some other players. In this paper, we propose and evaluate a Federated Learning based approach to train ML models for cognitive network management of future mobile networks that can enable network operators to get data analytics services by collaboratively building a shared learning model while retaining their critical data locally within their trusted domains.
https://doi.org/10.1109/NOMS54207.2022.9789936
Optimal input design and parameter estimation for continuous-time dynamical systems. - Ilmenau : Universitätsbibliothek, 2022. - 1 Online-Ressource (iv, 171 Seiten)
Technische Universität Ilmenau, Dissertation 2022
Diese Arbeit behandelt die Themengebiete Design of Experiments (DoE) und Parameterschätzung für zeitkontinuierliche Systeme, welche in der modernen Regelungstheorie eine wichtige Rolle spielen. Im gewählten Kontext untersucht DoE die Auswirkungen von verschiedenen Rahmenbedingungen von Simulations- bzw. Messexperimenten auf die Qualität der Parameterschätzung, wobei der Fokus auf der Anwendung der Theorie auf praxisrelevante Problemstellungen liegt. Dafür wird die weithin bekannte Fisher-Matrix eingeführt und die resultierende nicht lineare Optimierungsaufgabe angeschrieben. An einem PT1-System wird der Informationsgehalt von Signalen und dessen Auswirkungen auf die Parameterschätzung gezeigt. Danach konzentriert sich die Arbeit auf ein Teilgebiet von DoE, nämlich Optimal Input Design (OID), und wird am Beispiel eines 1D-Positioniersystems im Detail untersucht. Ein Vergleich mit häufig verwendeten Anregungssignalen zeigt, dass generierte Anregungssignale (OID) oft einen höheren Informationsgehalt aufweisen und mit genaueren Schätzwerten einhergeht. Zusätzlicher Benefit ist, dass Beschränkungen an Eingangs-, Ausgangs- und Zustandsgrößen einfach in die Optimierungsaufgabe integriert werden können. Der zweite Teil der Arbeit behandelt Methoden zur Parameterschätzung von zeitkontinuierlichen Modellen mit dem Fokus auf der Verwendung von Modulationsfunktionen (MF) bzw. Poisson-Moment Functionals (PMF) zur Vermeidung der zeitlichen Ableitungen und Least-Squares zur Lösung des resultierenden überbestimmten Gleichungssystems. Bei verrauschten Messsignalen ergibt sich daraus sofort die Problematik von nicht erwartungstreuen Schätzergebnissen (Bias). Aus diesem Grund werden Methoden zur Schätzung und Kompensation von Bias Termen diskutiert. Beitrag dieser Arbeit ist vor allem die detaillierte Aufarbeitung eines Ansatzes zur Biaskompensation bei Verwendung von PMF und Least-Squares für lineare Systeme und dessen Erweiterung auf (leicht) nicht lineare Systeme. Der vorgestellte Ansatz zur Biaskompensation (BC-OLS) wird am nicht linearen 1D-Servo in der Simulation und mit Messdaten validiert und in der Simulation mit anderen Methoden, z.B., Total-Least-Squares verglichen. Zusätzlich wird der Ansatz von PMF auf die weiter gefasste Systemklasse der Modulationsfunktionen (MF) erweitert. Des Weiteren wird ein praxisrelevantes Problem der Parameteridentifikation diskutiert, welches auftritt, wenn das Systemverhalten nicht gänzlich von der Identifikationsgleichung beschrieben wird. Am 1D-Servo wird gezeigt, dass ein Deaktivieren und Reaktivieren der PMF Filter mit geeigneter Initialisierung diese Problematik einfach löst.
https://doi.org/10.22032/dbt.52114
JUGGLER: autonomous cost optimization and performance prediction of big data applications. - In: SIGMOD '22, (2022), S. 1840-1854
Distributed in-memory processing frameworks accelerate iterative workloads by caching suitable datasets in memory rather than recomputing them in each iteration. Selecting appropriate datasets to cache as well as allocating a suitable cluster configuration for caching these datasets play a crucial role in achieving optimal performance. In practice, both are tedious, time-consuming tasks and are often neglected by end users, who are typically not aware of workload semantics, sizes of intermediate data, and cluster specification. To address these problems, we present Juggler, an end-to-end framework, which autonomously selects appropriate datasets for caching and recommends a correspondingly suitable cluster configuration to end users, with the aim of achieving optimal execution time and cost. We evaluate Juggler on various iterative, real-world, machine learning applications. Compared with our baseline, Juggler reduces execution time to 25.1% and cost to 58.1%, on average, as a result of selecting suitable datasets for caching. It recommends optimal cluster configuration in 50% of cases and near-to-optimal configuration in the remaining cases. Moreover, Juggler achieves an average performance prediction accuracy of 90%.
https://doi.org/10.1145/3514221.3517892
Joint model order estimation for multiple tensors with a coupled mode and applications to the joint decomposition of EEG, MEG Magnetometer, and Gradiometer tensors. - In: 2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, (2022), S. 1186-1190
The efficient estimation of an approximate model order is essential for applications with multidimensional data if the observed low-rank data is corrupted by additive noise. Certain signal processing applications such as biomedical studies, where the data are collected simultaneously through heterogeneous sensors, share some common features, i.e., coupled factors among multiple tensors. The exploitation of this coupling can lead to a better model order estimation, especially in case of low SNRs. In this paper, we extend the rank estimation techniques, designed for a single tensor, to noise-corrupted coupled low-rank tensors that share one of their factor matrices. To this end, we consider the joint effect of the global eigenvalues (calculated from the coupled HOSVD) and exploit the exponential behavior of the resulting coupled global eigenvalues. We show that the proposed method outperforms the classical criteria and can be successfully applied to EEG, MEG Magnetometer, and Gradiometer measurements. Our real data simulation results show that the estimated rank is highly reliable in terms of dominant components extraction.
https://doi.org/10.1109/ICASSP43922.2022.9747735
Cognitive network function for mobility robustness optimization in cellular networks. - In: 2022 IEEE Wireless Communications and Networking Conference (WCNC), (2022), S. 2025-2040
Self Organizing Networks (SON) aim at automating different network management functions, thereby improving their efficiency while reducing the operational expenditures. There are several proposed SON Functions (SFs) in the standards and a crucial one among them is Mobility Robustness Optimization (MRO). It focuses on providing seamless connectivity to mobile User Equipments (UEs). While optimizing handovers, there is a trade off between the Radio Link Failures (RLFs) and ping-pongs. Research has been widely done on the applicability of machine learning algorithms in SON for making decisions in a cognitive manner. In this study, MRO problem is modeled in two ways using two different classes of machine learning algorithms - Regression (linear and non-linear) and Recommender System. The work is evaluated on a Long Term Evolution (LTE) network simulator for different traffic scenarios. It is observed that the recommender system based solution has an edge over the regression based approaches and there is an overall improvement of 3.7% in the handover performance compared to that of the baseline approach.
https://doi.org/10.1109/WCNC51071.2022.9771898