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A wavelet-based sparse row-action method for image reconstruction in magnetic particle imaging
(2021)
Purpose
Magnetic particle imaging (MPI) is a preclinical imaging technique capable of visualizing the spatio-temporal distribution of magnetic nanoparticles. The image reconstruction of this fast and dynamic process relies on efficiently solving an ill-posed inverse problem. Current approaches to reconstruct the tracer concentration from its measurements are either adapted to image characteristics of MPI but suffer from higher computational complexity and slower convergence or are fast but lack in the image quality of the reconstructed images.
Methods
In this work we propose a novel MPI reconstruction method to combine the advantages of both approaches into a single algorithm. The underlying sparsity prior is based on an undecimated wavelet transform and is integrated into a fast row-action framework to solve the corresponding MPI minimization problem.
Results
Its performance is numerically evaluated against a classical FISTA (Fast Iterative Shrinkage-Thresholding Algorithm) approach on simulated and real MPI data. The experimental results show that the proposed method increases image quality with significantly reduced computation times.
Conclusions
In comparison to state-of-the-art MPI reconstruction methods, our approach shows better reconstruction results and at the same time accelerates the convergence rate of the underlying row-action algorithm.
Circular Innovator Education
(2021)
The following article is a first step towards raising the awareness of these new trainers (who are conscious and innovators themselves) and giving them methods, tools, and inspiration to develop sustainable innovators to think holistically. Innovators are the proportion of entrepreneurs who do not copy or optimize what already exists but commercialize new technologies or new ideas. Circular innovators are the doers who create systemic innovations solving our current social and ecological crisis.
In this chapter, the EIL (Effective Innovation Leadership) framework is tested empirically. First, peer-reviewed journals in the innovation management, leadership, and transformation discipline are analyzed. Second, a pre-test with 58 executives takes place. The response behavior of the participants varies depending on the company's degree of digital maturity. Third, 20 innovation leaders employed in mature digital companies answer the survey. The participants perceive their company as innovative and state that up to 89% of created innovations are digital. Values relevant to digital innovation leaders are innovation, responsibility, positivity, and transparency. Relevant strengths are creativity and learning. Both strongly correlate with a few efficacy items. Decisiveness correlates with innovation strategy. Entrepreneurship, self-regulation, and culture correlate with each other. Creativity connects the value of innovation and the practice of communication. The insights from this chapter contribute to building a reliable and valid factor-based effective digital innovation leadership questionnaire in the future.
Design Rules for Hybrid Additive Manufacturing Combining Selective Laser Melting and Micromilling
(2021)
We report on a comprehensive study to evaluate fundamental properties of a hybrid
manufacturing approach, combining selective laser melting and high speed milling, and to characterize
typical geometrical features and conclude on a catalogue of design rules. As for any additive
manufacturing approach, the understanding of the machine properties and the process behaviour as
well as such a selection guide is of upmost importance to foster the implementation of new machining
concepts and support design engineers. Geometrical accuracy between digitally designed and
physically realized parts made of maraging steel and dimensional limits are analyzed by stripe line
projection. In particular, we identify design rules for numerous basic geometric elements like walls,
cylinders, angles, inclinations, overhangs, notches, inner and outer radii of spheres, chamfers in build
direction, and holes of different shape, respectively, as being manufactured by the hybrid approach
and compare them to sole selective laser melting. While the cutting tool defines the manufacturability
of, e.g., edges and corners, the milling itself improves the surface roughness to Ra < 2 µm. Thus,
the given advantages of this hybrid process, e.g., space-resolved and custom-designed roughness
and the superior geometrical accuracy are evaluated. Finally, we exemplify the potential of this
particular promising hybrid approach by demonstrating an injection mold with a conformal cooling
for a charge socket for an electro mobile
Manipulation von Abgaswerten
(2021)
Dieses Buch stellt Hintergründe zum Abgasskandal dar, z. B. welche Schadstoffe kritisch sind, wie Motorsteuergeräte, Abgasrückführung und Abgasnachbehandlung funktionieren, wie Manipulationen auf dem Prüfstand möglich sind, mit welchen Fahrzyklen getestet wird und in welchem rechtlichen und politischen Umfeld dies lange Zeit geschehen konnte. Es werden Maßnahmen vorgestellt, mit denen Fahrzeuge auch real Grenzwerte einhalten können und wie Manipulationen zukünftig rechtlich und politisch verhindert werden können. Dem Leser werden durch Literaturhinweise zusätzliche Details erschlossen.
Advanced Control Methods for High Performance Dual Two-Level Inverter for Traction Application
(2021)
The topology of the dual two-level inverter in combination with an electrical machine with open-end winding is an optimal
solution for the realization of high requirements from the automotive sector for high-performance traction drives. In this
publication, methods for extending the operating range and improving output voltage and current quality are presented
for synchronous as well as asynchronous PWM methods for the entire operating range for this system configuration.
Emphasis is placed on symmetrical power sharing between the inverters to maximize the power range. Optimal integration
of the system is achieved by the topology-related possibility of DC fast charging of the energy storages from a DC grid
without an additional charging unit.
This paper presents a new self-learning control scheme for lateral track guidance of industrial trucks using artificial intelligence. It is an universally applicable lateral dynamic control concept which is able to adapt itself to different truck variants. Moreover it shall consider vehicle parameter variations that occur during operation, such as the load dependent change of vehicle mass and moment of inertia. The proposed approach uses Reinforcement Learning. In order to reduce the training effort, a new concept is realized, taking into account a priori knowledge of vehicle behavior. Its fundamental idea consists of dividing the training process into two steps. In the first step the controller will be pre-trained on basis of a nominal model representing a priori knowledge of lateral dynamic vehicle behavior. Since this model is derived for an industrial truck with average vehicle parameter values, a fine tuning of the control parameters has to be performed in the second step. In this way the controller is adapted to the actual truck variant and the corresponding vehicle parameter values. In order to demonstrate the efficiency of the proposed control scheme, the simulation results given in this paper are compared to the closed loop behavior using standard LQR.