arXiv subjects

Christian Becker

Publications and source records attributed to Christian Becker.

At least 19 recordsLinked to original sources

arXiv2025

Minimize Your Critical Path with Combine-and-Exchange Locks

Coroutines are experiencing a renaissance as many modern programming languages support the use of cooperative multitasking for highly parallel or asynchronous applications. One of the greatest advantages of this is that concurrency and synchronization is manged entirely in the userspace, omitting heavy-weight system calls. However, we find that state-of-the-art userspace synchronization primitives approach synchronization in the userspace from the perspective of kernel-level scheduling. This introduces unnecessary delays on the critical path of the application, limiting throughput. In this paper, we re-think synchronization for tasks that are scheduled entirely in the userspace (e.g., coroutines, fibers, etc.). We develop Combine-and-Exchange Scheduling (CES), a novel scheduling approach that ensures contended critical sections stay on the same thread of execution while parallelizable work is evenly spread across the remaining threads. We show that our approach can be applied to many existing languages and libraries, resulting in 3-fold performance improvements in application benchmarks as well as 8-fold performance improvements in microbenchmarks.

cs.DC↗

arXiv2025

Unified Graph-Theoretic Modeling of Multi-Energy Flows in Distribution Systems

The increasing complexity of energy systems due to sector coupling and decarbonization calls for unified modeling frameworks that capture the physical and structural interactions between electricity, gas, and heat networks. This paper presents a graph-based modeling approach for multi-energy systems, where each domain is represented as a layer in a multi-layer graph, and coupling technologies are modeled as inter-layer edges via a dedicated coupling layer. A steady-state solver based on a block-structured Newton-Raphson method is developed to jointly compute flows and state variables across all carriers. The proposed model is tested and validated on a realistic case study based on data from a German distribution network. The results demonstrate convergence, numerical accuracy, and consistent domain interaction, and demonstrate the method's applicability for system-wide analysis and its potential as a foundation for future optimizations in integrated energy systems.

eess.SY↗

arXiv2025

An (m,k)-firm Elevation Policy for Weakly Hard Real-Time in Converged 5G-TSN Networks

Ongoing standardization efforts in 5G and Time-Sensitive Networking (TSN) aim to provide safety-critical applications with real-time communication. However, 5G-TSN network schedules often rely on idealistic delay models that can jeopardize the validity of their guarantees. This paper presents an $(m,k)$-firm Elevation Policy to uphold a base level of weakly hard real-time guarantees (WHRT). It augments the primary schedule with a dynamic priority-driven scheme to elevate the priority of $m$ out of $k$ consecutive frames if they experience unexpected delays. Our evaluations demonstrate the necessity of WHRT to increase fault-tolerance against 5G delay outliers and to uphold the quality of control within a 5G-TSN networked control system. Still, only a small resource overhead is imposed during epochs where the primary schedule is valid and can serve stronger QoS guarantees. The $(m,k)$-firm Elevation Policy thereby yields a robust but light-weight fallback mechanism to serve applications with dependable guarantees during unstable network conditions.

cs.NI↗

arXiv2024

Physics-informed Actor-Critic for Coordination of Virtual Inertia from Power Distribution Systems

The vanishing inertia of synchronous generators in transmission systems requires the utilization of renewables for inertial support. These are often connected to the distribution system and their support should be coordinated to avoid violation of grid limits. To this end, this paper presents the Physics-informed Actor-Critic (PI-AC) algorithm for coordination of Virtual Inertia (VI) from renewable Inverter-based Resources (IBRs) in power distribution systems. Acquiring a model of the distribution grid can be difficult, since certain parts are often unknown or the parameters are highly uncertain. To favor model-free coordination, Reinforcement Learning (RL) methods can be employed, necessitating a substantial level of training beforehand. The PI-AC is a RL algorithm that integrates the physical behavior of the power system into the Actor-Critic (AC) approach in order to achieve faster learning. To this end, we regularize the loss function with an aggregated power system dynamics model based on the swing equation. Throughout this paper, we explore the PI-AC functionality in a case study with the CIGRE 14-bus and IEEE 37-bus power distribution system in various grid settings. The PI-AC is able to achieve better rewards and faster learning than the exclusively data-driven AC algorithm and the metaheuristic Genetic Algorithm (GA).

eess.SY↗

arXiv2024

Bayesian Physics-informed Neural Networks for System Identification of Inverter-dominated Power Systems

While the uncertainty in generation and demand increases, accurately estimating the dynamic characteristics of power systems becomes crucial for employing the appropriate control actions to maintain their stability. In our previous work, we have shown that Bayesian Physics-informed Neural Networks (BPINNs) outperform conventional system identification methods in identifying the power system dynamic behavior under measurement noise. This paper takes the next natural step and addresses the more significant challenge, exploring how BPINN perform in estimating power system dynamics under increasing uncertainty from many Inverter-based Resources (IBRs) connected to the grid. These introduce a different type of uncertainty, compared to noisy measurements. The BPINN combines the advantages of Physics-informed Neural Networks (PINNs), such as inverse problem applicability, with Bayesian approaches for uncertainty quantification. We explore the BPINN performance on a wide range of systems, starting from a single machine infinite bus (SMIB) system and 3-bus system to extract important insights, to the 14-bus CIGRE distribution grid, and the large IEEE 118-bus system. We also investigate approaches that can accelerate the BPINN training, such as pretraining and transfer learning. Throughout this paper, we show that in presence of uncertainty, the BPINN achieves orders of magnitude lower errors than the widely popular method for system identification SINDy and significantly lower errors than PINN, while transfer learning helps reduce training time by up to 80 %.

eess.SY↗

arXiv2023

Strategic Planning of Carbon-Neutral Heating Demand Coverage Under Uncertainty in a Coupled Multi-Energy Grid

Integrating the gas and district heating with the electrical grid in a multi-energy grid has been shown to provide flexibility and prevent bottlenecks in the operation of electrical distribution grids. This integration assumes a top-down grid planning approach and a perfect knowledge of consumer behaviour. In reality, consumers decides whether to adopt a heating technology based on costs and government regulation. This behavior is highly uncertain and depends on fluctuations in heating technology costs and energy prices. The uncertainty associated with consumer behavior increases the risk of investment in grid expansion. In response to this challenge, this paper proposes an approach with the consumer at the center of the planning method. Robust optimization is used to model the uncertainty in prices to reduce the risk of investment in grid expansion. The uncertainty in energy prices is modeled using interval uncertainty with a proportional deviation. This allows planners, operators and regulators to predict the adoption rate of certain heating technology in different geographical areas and prioritize the expansion of specific grids where they are required. By minimizing a cost function subject to robust constraints, the strategy ensures robustness against uncertainties in energy prices. This robust optimization approach is applied to Hamburg as a case study. The result of the optimization represents the consumer's decision. The impact of the consumer's decision on the electrical grid is analzed on different benchmark distribution grids. The study concludes that district heating expansion in high-density areas is a low-risk investment for carbon neutrality. In less dense areas, electrification supports decentralized heat pumps. Meanwhile, hydrogen gas grids are viable where electric expansion is impractical. Increased uncertainty leads to more conservative solutions.

eess.SY↗

arXiv2023

Towards a more comprehensive open-source model for interdisciplinary smart integrated energy systems

The energy transition has recently experienced a further acceleration. In order to make the integration of renewable energies as cost-effective, secure and sustainable as possible and to develop new paradigms for the energy system, many energy system models have been developed in research in the past to evaluate the solutions. While model identification and dissemination of results are widely discussed in the literature, a detailed view of the methodology is often missing. This paper addresses this topic and proposes a methodology to build a comprehensive, publicly accessible database for modeling a multi-modal integrated energy system. The focus hereby is dynamic modeling of low- and medium-voltage grids consisting of prosumers, battery storages, heat pumps and electric cars. In addition, a district heating network is parameterized to match the electricity grid. Modelica and the TransiEnt-Library serves as the modeling tool. The methodology for creating the grid models is available via GitLab. A study case that uses the methodology to analyze the congestion situation within a medium-voltage distribution grid is presented.

eess.SY↗

arXiv2023

Optimized utilization of decentral flexibility for the operational management of cellular multi-modal distribution grids

Future energy systems rely mostly on supply-dependent resources like wind and solar energy. Since most of the produced energy from distributed renewable sources arises as electricity, electrification of energy consumers and producers, called prosumers, expands. In this context, multi-modal distribution grids make use of the storage capabilities of other energy sectors like heat, and abolish the supply-dependency and uncertainty of the feed-in of decentralized generators. This provides flexibility in the energy system, which increases security of supply and at the same time ensures economical energy provision. The subject of this paper is to exploit the flexibility of such a multi-modal energy system for operation in an optimal way. The cellular approach serves here as the energy system architecture. Aim is to provide flexibility with a cell by optimally distributing a flexibility request to subordinate prosumers changing their active and reactive power. The load change is hereby to be optimally dispatched to the different components of the prosumers under consideration of technical restrictions and dynamic behavior of components, the grids and economical aspects. For dynamic modelling and simulation the open-source modelling language Modelica is used. Using a low-voltage benchmark model expanded by multi-modal technologies, the proposed methodology shows that the cells are capable of providing flexibility to the overlying grid with high accuracy at low costs.

eess.SY↗

arXiv2023

Integrated Planning of Multi-energy Grids: Concepts and Challenges

In order to meet ever-stricter climate targets and achieve the eventual decarbonization of the energy supply of German industrial metropolises, the focus is on gradually phasing out nuclear power, then coal and gas combined with the increased use of renewable energy sources and employing hydrogen as a clean energy carrier. While complete electrification of the energy supply of households and the transportation sector may be the ultimate goal, a transitional phase is necessary as such massive as well as rapid expansion of the electrical distribution grid is infeasible. Additionally, German industries have expressed their plans to use hydrogen as their primary strategy in meeting carbon targets. This poses challenges to the existing electrical, gas, and heating distribution grids. It becomes necessary to integrate the planning and developing procedures for these grids to maximize efficiencies and guarantee security of supply during the transition. The aim of this paper is thus to highlight those challenges and present novel concepts for the integrated planning of the three grids as one multi-energy grid.

eess.SY↗

arXiv2022

Bayesian Physics-Informed Neural Networks for Robust System Identification of Power Systems

This paper introduces for the first time, to the best of our knowledge, the Bayesian Physics-Informed Neural Networks for applications in power systems. Bayesian Physics-Informed Neural Networks (BPINNs) combine the advantages of Physics-Informed Neural Networks (PINNs), being robust to noise and missing data, with Bayesian modeling, delivering a confidence measure for their output. Such a confidence measure can be very valuable for the operation of safety critical systems, such as power systems, as it offers a degree of trustworthiness for the neural network output. This paper applies the BPINNs for robust identification of the system inertia and damping, using a single machine infinite bus system as the guiding example. The goal of this paper is to introduce the concept and explore the strengths and weaknesses of BPINNs compared to existing methods. We compare BPINNs with the PINNs and the recently popular method for system identification, SINDy. We find that BPINNs and PINNs are robust against all noise levels, delivering estimates of the system inertia and damping with significantly lower error compared to SINDy, especially as the noise levels increases.

eess.SY↗

arXiv2021

Roadmap for Edge AI: A Dagstuhl Perspective

Based on the collective input of Dagstuhl Seminar (21342), this paper presents a comprehensive discussion on AI methods and capabilities in the context of edge computing, referred as Edge AI. In a nutshell, we envision Edge AI to provide adaptation for data-driven applications, enhance network and radio access, and allow the creation, optimization, and deployment of distributed AI/ML pipelines with given quality of experience, trust, security and privacy targets. The Edge AI community investigates novel ML methods for the edge computing environment, spanning multiple sub-fields of computer science, engineering and ICT. The goal is to share an envisioned roadmap that can bring together key actors and enablers to further advance the domain of Edge AI.

cs.DC↗

arXiv2020

A Survey on Predictive Maintenance for Industry 4.0

Production issues at Volkswagen in 2016 lead to dramatic losses in sales of up to 400 million Euros per week. This example shows the huge financial impact of a working production facility for companies. Especially in the data-driven domains of Industry 4.0 and Industrial IoT with intelligent, connected machines, a conventional, static maintenance schedule seems to be old-fashioned. In this paper, we present a survey on the current state of the art in predictive maintenance for Industry 4.0. Based on a structured literate survey, we present a classification of predictive maintenance in the context of Industry 4.0 and discuss recent developments in this area.

cs.LG↗

arXiv2020

A Survey on Human Machine Interaction in Industry 4.0

Industry 4.0 or Industrial IoT both describe new paradigms for seamless interaction between humans and machines. Both concepts rely on intelligent, inter-connected cyber-physical production systems that are able to control the process flow of industrial production. As those machines take many decisions autonomously and further interact with production and manufacturing planning systems, the integration of human users requires new paradigms. In this paper, we provide an analysis of the current state-of-the-art in human-machine interaction in the Industry 4.0 domain.We focus on new paradigms that integrate the application of augmented and virtual reality technology. Based on our analysis, we further provide a discussion of research challenges.

cs.HC↗

arXiv2015

Abelian duality on globally hyperbolic spacetimes

We study generalized electric/magnetic duality in Abelian gauge theory by combining techniques from locally covariant quantum field theory and Cheeger-Simons differential cohomology on the category of globally hyperbolic Lorentzian manifolds. Our approach generalizes previous treatments using the Hamiltonian formalism in a manifestly covariant way and without the assumption of compact Cauchy surfaces. We construct semi-classical configuration spaces and corresponding presymplectic Abelian groups of observables, which are quantized by the CCR-functor to the category of $C^*$-algebras. We demonstrate explicitly how duality is implemented as a natural isomorphism between quantum field theories. We apply this formalism to develop a fully covariant quantum theory of self-dual fields.

hep-th↗

arXiv2015

Cheeger-Simons differential characters with compact support and Pontryagin duality

By adapting the Cheeger-Simons approach to differential cohomology, we establish a notion of differential cohomology with compact support. We show that it is functorial with respect to open embeddings and that it fits into a natural diagram of exact sequences which compare it to compactly supported singular cohomology and differential forms with compact support, in full analogy to ordinary differential cohomology. We prove an excision theorem for differential cohomology using a suitable relative version. Furthermore, we use our model to give an independent proof of Pontryagin duality for differential cohomology recovering a result of [Harvey, Lawson, Zweck - Amer. J. Math. 125 (2003) 791]: On any oriented manifold, ordinary differential cohomology is isomorphic to the smooth Pontryagin dual of compactly supported differential cohomology. For manifolds of finite-type, a similar result is obtained interchanging ordinary with compactly supported differential cohomology.

math.DG↗

arXiv2014

Differential cohomology and locally covariant quantum field theory

We study differential cohomology on categories of globally hyperbolic Lorentzian manifolds. The Lorentzian metric allows us to define a natural transformation whose kernel generalizes Maxwell's equations and fits into a restriction of the fundamental exact sequences of differential cohomology. We consider smooth Pontryagin duals of differential cohomology groups, which are subgroups of the character groups. We prove that these groups fit into smooth duals of the fundamental exact sequences of differential cohomology and equip them with a natural presymplectic structure derived from a generalized Maxwell Lagrangian. The resulting presymplectic Abelian groups are quantized using the CCR-functor, which yields a covariant functor from our categories of globally hyperbolic Lorentzian manifolds to the category of C*-algebras. We prove that this functor satisfies the causality and time-slice axioms of locally covariant quantum field theory, but that it violates the locality axiom. We show that this violation is precisely due to the fact that our functor has topological subfunctors describing the Pontryagin duals of certain singular cohomology groups. As a byproduct, we develop a Fr\'echet-Lie group structure on differential cohomology groups.

hep-th↗

arXiv2014

Cheeger-Chern-Simons theory and differential String classes

We introduce certain relative differential characters which we call Cheeger-Chern-Simons characters. These combine the well-known Cheeger-Simons characters with Chern-Simons forms. In the same way as the Cheeger-Simons characters generalize Chern-Simons invariants of oriented closed manifolds, the Cheeger-Chern-Simons characters generalize Chern-Simons invariants of oriented manifolds with boundary. Using Cheeger-Chern-Simons characters, we introduce the notion of differential trivializations of universal characteristic classes. Specializing to the class 1/2 $p_1 \in H^4(B\mathrm{Spin}_n;\mathbb Z)$ this yields a notion of differential String classes. Differential String classes turn out to be stable isomorphism classes of geometric String structures.

math.DG↗

arXiv2013

Relative differential cohomology

We study two notions of relative differential cohomology, using the model of differential characters. The two notions arise from the two options to construct relative homology, either by cycles of a quotient complex or of a mapping cone complex. We discuss the relation of the two notions of relative differential cohomology to each other. We discuss long exact sequences for both notions, thereby clarifying their relation to absolute differential cohomology. We construct the external and internal product of relative and absolute characters and show that relative differential cohomology is a right module over the absolute differential cohomology ring. Finally we construct fiber integration and transgression for relative differential characters.

math.DG↗