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Die Einhaltung der innerhalb der Designphase festgelegten Architektur eines Softwareprojektes muss w ̈ahrend der Entwicklungsphase sichergestellt werden. Dieses Papier beschreibt eine Erweiterung des Eclipse-Plugins JDepend4Eclipse, die die Verwaltung von Regels ̈atzen erlaubt und die Pr ̈ufung auf in einem Projekt vorhandene, unerlaubte Abh ̈angigkeiten auf Knopfdruck innerhalb der Entwicklungsumgebung vornimmt. Die Erweiterung des Plugins wird bereits erfolgreich in internen Projekten der Hochschule Offenburg eingesetzt und soll demn ̈achst ̈offentlich verf ̈ugbar sein.
The sharp rise in electricity and oil prices due to the war in Ukraine has caused fluctuations in the results of the previous study about the economic analysis of electric buses. This paper shows how the increase in fuel prices affects the implementation of electric buses. This publication is constructing the Total Cost of Ownership (TCO) model in the small-mid-size city, Offenburg for the transition to electric buses. The future development of costs is estimated and a projection based on learning curves will be carried out. This study intends to introduce a new future prospect by presenting the latest data based on previous research. Through the new TCO result, the cost differences between the existing diesel bus and the electric bus are updated, and also the future prospects for the economic feasibility of the electric bus in a small and midsize city are presented.
While most ultrafast time-resolved optical pump-probe experiments in magnetic materials reveal the spatially homogeneous magnetization dynamics of ferromagnetic resonance (FMR), here we explore the magneto-elastic generation of GHz-to-THz frequency spin waves (exchange magnons). Using analytical magnon oscillator equations, we apply time-domain and frequency-domain approaches to quantify the results of ultrafast time-resolved optical pump-probe experiments in free-standing ferromagnetic thin films. Simulations show excellent agreement with the experiment, provide acoustic and magnetic (Gilbert) damping constants and highlight the role of symmetry-based selection rules in phonon-magnon interactions. The analysis is extended to hybrid multilayer structures to explore the limits of resonant phonon-magnon interactions up to THz frequencies.
Generative machine learning models for creative purposes play an increasingly prominent role in the field of dance and technology. A particularly popular approach is the use of such models for generating synthetic motions. Such motions can either serve as source of ideation for choreographers or control an artificial dancer that acts as improvisation partner for human dancers. Several examples employ autoencoder-based deep-learning architectures that have been trained on motion capture recordings of human dancers. Synthetic motions are then generated by navigating the autoencoder's latent space. This paper proposes an alternative approach of using an autoencoder for creating synthetic motions. This approach controls the generation of synthetic motions on the level of the motion itself rather than its encoding. Two different methods are presented that follow this principle. Both methods are based on the interactive control of a single joint of an artificial dancer while the other joints remain under the control of the autoencoder. The first method combines the control of the orientation of a joint with iterative autoencoding. The second method combines the control of the target position of a joint with forward kinematics and the application of latent difference vectors. As illustrative example of an artistic application, this latter method is used for an artificial dancer that plays a digital instrument. The paper presents the implementation of these two methods and provides some preliminary results.
It is common practice to apply padding prior to convolution operations to preserve the resolution of feature-maps in Convolutional Neural Networks (CNN). While many alternatives exist, this is often achieved by adding a border of zeros around the inputs. In this work, we show that adversarial attacks often result in perturbation anomalies at the image boundaries, which are the areas where padding is used. Consequently, we aim to provide an analysis of the interplay between padding and adversarial attacks and seek an answer to the question of how different padding modes (or their absence) affect adversarial robustness in various scenarios.
Im vorliegenden Beitrag wird ein Strommarktsimulationsmodell entwickelt, mit dessen Hilfe die Bereitstellung von Flexibilität auf dem Strom- und Regelleistungsmarkt in Deutschland modell-gestützt analysiert werden soll. Das Modell bildet dabei zwei parallel verlaufende, zentrale Wettbewerbsmärkte ab, an denen Akteure durch die individuelle Gebotsermittlung handeln können. Die entsprechend hierzu entwickelte Gebotslogik wird detailliert erläutert, wobei der Fokus auf der Flexibilität fossil-thermischer Kraftwerke liegt. In der anschließenden Gegen-überstellung mit realen Marktpreisen zeigt sich, dass die verwendete Methodik und die Ge-botslogik den bestehenden Markt und dessen Marktergebnis in geeigneter Form wiederspie-geln, wodurch zukünftig unterschiedlichste Flexibilitätsszenarien analysiert und Aussagen zu deren Auswirkungen auf den Markt und seine Akteure getroffen werden können.
This paper describes a taxonomy which allows to assess and compare different implementations of master data objects. A systematic breakdown of core entities provides a framework to tell apart four subdividing categories of master data objects: independent and dependent objects, relational objects, and reference objects that serve to attribute information. This supports the preparation of data migrations from one system to another.
An import ban of Russian energy sources to Germany is currently being increasingly discussed. We want to support the discussion by showing a way how the electricity system in Germany can manage low energy imports in the short term and which measures are necessary to still meet the climate protection targets. In this paper, we examine the impact of a complete stop of Russian fossil fuel imports on the electricity sector in Germany, and how this will affect the climate coals of an earlier coal phase-out and climate neutrality by 2045.
Following a scenario-based analysis, the results gave a point of view on how much would be needed to completely rely on the scarce non-renewable energy resources in Germany. Huge amounts of investments would be needed in order to ensure a secure supply of electricity, in both generation energy sources (RES) and energy storage systems (ESS). The key findings are that a rapid expansion of renewables and storage technologies will significantly reduce the dependence of the German electricity system on energy imports. The huge integration of renewable energy does not entail any significant imports of the energy sources natural gas, hard coal, and mineral oil, even in the long term. The results showed that a ban on fossil fuel imports from Russia outlines huge opportunities to go beyond the German government's climate targets, where the 1.5-degree-target is achieved in the electricity system.
To achieve Germany's climate targets, the industrial sector, among others, must be transformed. The decarbonization of industry through the electrification of heating processes is a promising option. In order to investigate this transformation in energy system models, high-resolution temporal demand profiles of the heat and electricity applications for different industries are required. This paper presents a method for generating synthetic electricity and heat load profiles for 14 industry types. Using this methodology, annual profiles with a 15-minute resolution can be generated for both energy demands. First, daily profiles for the electricity demand were generated for 4 different production days. These daily profiles are additionally subdivided into eight end-use application categories. Finally, white noise is applied to the profile of the mechanical drives. The heat profile is similar to the electrical but is subdivided into four temperature ranges and the two applications hot water and space heating. The space heating application is additionally adjusted to the average monthly outdoor temperature. Both time series were generated for the analysis of an electrification of industrial heat application in energy system modelling.
Peer-to-peer energy trading and local electricity markets have been widely discussed as new options for the transformation of the energy system from the traditional centralized scheme to the novel decentralized one. Moreover, it has also been proposed as a more favourable alternative for already expiring feed in tariff policies that promote investment in renewable energy sources. Peer-to-peer energy trading is usually defined as the integration of several innovative technologies, that enable both prosumers and consumers to trade electricity, without intermediaries, at a consented price. Furthermore, the techno-economic aspects go hand in hand with the socio-economic aspects, which represent at the end significant barriers that need to be tackled to reach a higher impact on current power systems. Applying a qualitative analysis, two scalable peer-to-peer concepts are presented in this study and the possible participant´s entry probability into such concepts. Results show that consumers with a preference for environmental aspects have in general a higher willingness to participate in peer-to-peer energy trading. Moreover, battery storage systems are a key technology that could elevate the entry probability of prosumers into a peer-to-peer market.
This paper describes a thorough analysis of using PPO to learn kick behaviors with simulated NAO robots in the simspark environment. The analysis includes an investigation of the influence of PPO hyperparameters, network size, training setups and performance in real games. We believe to improve the state of the art mainly in four points: first, the kicks are learned with a toed version of the NAO robot, second, we improve the reliability with respect to kickable area and avoidance of falls, third, the kick can be parameterized with desired distance and direction as input to the deep network and fourth, the approach allows to integrate the learned behavior seamlessly into soccer games. The result is a significant improvement of the general level of play.
The energy system is changing since some years in order to achieve the climate goals from the Paris Agreement which wants to prevent an increase of the global temperature above 2 °C [1]. Decarbonisation of the energy system has become for governments a big challenge and different strategies are being stablished. Germany has set greenhouse gas reduction limits for different years and keeps track of the improvement made yearly. The expansion of renewable energy systems (RES) together with decarbonisation technologies are a key factor to accomplish this objective.
This research is done to analyse the effect of introducing biochar, a decarbonisation technology, and study how it will affect the energy system. Pyrolysis is the process from which biochar is obtained and it is modelled in an open-source energy system model. A sensibility analysis is done in order to assess the effect of changing the biomass potential and the costs for pyrolysis.
The role of pyrolysis is analysed in the form of different future scenarios for the year 2045 to evaluate the impact when the CO2 emission limit is zero. All scenarios are compared to the reference scenario, where pyrolysis is not considered.
Results show that biochar can be used to compensate the emissions from other conventional power plant and achieve an energy transition with lower costs. Furthermore, it was also found that pyrolysis can also reduce the need of flexibility. This study also shows that the biomass potential and the pyrolysis costs can strongly affect the behaviour of pyrolysis in the energy system.
Bio, vegan – oder was?
(2023)
Nachhaltigkeit als gesellschaftlicher Wert beeinflusst auch die Haltung der Konsumierenden gegenüber Fleisch- und Wurstkonsum und kann zum Umkippen bisheriger Konsummuster führen (Tipping-Point). Für EDEKA Südwestfleisch und Schwarzwaldhof erfordert dies – aufbauend auf der bisherigen Ausrichtung an Nachhaltigkeit – eine zukunftsorientierte Planung des Sortiments im veganen, vegetarischen, hybriden Sektor und im Bereich Bio-Produkte und Tierwohl. Hierfür muss auch die Kommunikationspolitik angepasst werden, um jüngere Zielgruppen zu erreichen, damit das Dilemma der Fleischwirtschaft (Tierwohl wird gefordert, aber nicht in gleichem Masse gekauft) nicht zu Lasten des Markterfolgs geht.
Beuys-Gespräch
(2022)
We revisit the quantitative analysis of the ultrafast magnetoacoustic experiment in a freestanding nickel thin film by Kim and Bigot [J.-W. Kim and J.-Y. Bigot, Phys. Rev. B 95, 144422 (2017)] by applying our recently proposed approach of magnetic and acoustic eigenmode decomposition. We show that the application of our modeling to the analysis of time-resolved reflectivity measurements allows for the determination of amplitudes and lifetimes of standing perpendicular acoustic phonon resonances with unprecedented accuracy. The acoustic damping is found to scale as ∝ω2 for frequencies up to 80 GHz, and the peak amplitudes reach 10−3. The experimentally measured magnetization dynamics for different orientations of an external magnetic field agrees well with numerical solutions of magnetoelastically driven magnon harmonic oscillators. Symmetry-based selection rules for magnon-phonon interactions predicted by our modeling approach allow for the unambiguous discrimination between spatially uniform and nonuniform modes, as confirmed by comparing the resonantly enhanced magnetoelastic dynamics simultaneously measured on opposite sides of the film. Moreover, the separation of timescales for (early) rising and (late) decreasing precession amplitudes provide access to magnetic (Gilbert) and acoustic damping parameters in a single measurement.
Neural networks have a number of shortcomings. Amongst the severest ones is the sensitivity to distribution shifts which allows models to be easily fooled into wrong predictions by small perturbations to inputs that are often imperceivable to humans and do not have to carry semantic meaning. Adversarial training poses a partial solution to address this issue by training models on worst-case perturbations. Yet, recent work has also pointed out that the reasoning in neural networks is different from humans. Humans identify objects by shape, while neural nets mainly employ texture cues. Exemplarily, a model trained on photographs will likely fail to generalize to datasets containing sketches. Interestingly, it was also shown that adversarial training seems to favorably increase the shift toward shape bias. In this work, we revisit this observation and provide an extensive analysis of this effect on various architectures, the common L_2-and L_-training, and Transformer-based models. Further, we provide a possible explanation for this phenomenon from a frequency perspective.