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We propose a new training method to improve HMM-based keyword spotting. The loss function is based on a score computed with the keyword/filler model from the entire input sequence. It is equivalent to max/attention pooling but is based on prior acoustic knowledge. We also employ a multi-task learning setup by predicting both LVCSR and keyword posteriors. We compare our model to a baseline trained on frame-wise cross entropy, with and without per-class weighting. We employ a low-footprint TDNN for acoustic modeling. The proposed training yields significant and consistent improvement over the baseline in adverse noise conditions. The FRR on cafeteria noise is reduced from 13.07% to 5.28% at 9 dB SNR and from 37.44% to 6.78% at 5 dB SNR. We obtain these results with only 600 unique training keyword samples. The training method is independent of the frontend and acoustic model topology.
Deep neural networks (DNN) have recently been widely used in speaker recognition systems, achieving state-of-the-art performance on various benchmarks. The x-vector architecture is especially popular in this research community, due to its excellent performance and manageable computational complexity. In this paper, we present the lrx-vector system, which is the low-rank factorized version of the x-vector embedding network. The primary objective of this topology is to further reduce the memory requirement of the speaker recognition system. We discuss the deployment of knowledge distillation for training the lrx-vector system and compare against low-rank factorization with SVD. On the VOiCES 2019 far-field corpus we were able to reduce the weights by 28% compared to the full-rank x-vector system while keeping the recognition rate constant (1.83% EER).
Speaker recognition performance has been greatly improved with the emergence of deep learning. Deep neural networks show the capacity to effectively deal with impacts of noise and reverberation, making them attractive to far-field speaker recognition systems. The x-vector framework is a popular choice for generating speaker embeddings in recent literature due to its robust training mechanism and excellent performance in various test sets. In this paper, we start with early work on including invariant representation learning (IRL) to the loss function and modify the approach with centroid alignment (CA) and length variability cost (LVC) techniques to further improve robustness in noisy, far-field applications. This work mainly focuses on improvements for short-duration test utterances (1-8s). We also present improved results on long-duration tasks. In addition, this work discusses a novel self-attention mechanism. On the VOiCES far-field corpus, the combination of the proposed techniques achieves relative improvements of 7.0% for extremely short and 8.2% for full-duration test utterances on equal error rate (EER) over our baseline system.
Parkinson's disease is a neurodegenerative disorder characterized by the presence of different motor impairments. Information from speech, handwriting, and gait signals have been considered to evaluate the neurological state of the patients. On the other hand, user models based on Gaussian mixture models - universal background models (GMM-UBM) and i-vectors are considered the state-of-the-art in biometric applications like speaker verification because they are able to model specific speaker traits. This study introduces the use of GMM-UBM and i-vectors to evaluate the neurological state of Parkinson's patients using information from speech, handwriting, and gait. The results show the importance of different feature sets from each type of signal in the assessment of the neurological state of the patients.
We propose a new training method to improve HMM-based keyword spotting. The loss function is based on a score computed with the keyword/filler model from the entire input sequence. It is equivalent to max/attention pooling but is based on prior acoustic knowledge. We also employ a multi-task learning setup by predicting both LVCSR and keyword posteriors. We compare our model to a baseline trained on frame-wise cross entropy, with and without per-class weighting. We employ a low-footprint TDNN for acoustic modeling. The proposed training yields significant and consistent improvement over the baseline in adverse noise conditions. The FRR on cafeteria noise is reduced from 13.07% to 5.28% at 9 dB SNR and from 37.44% to 6.78% at 5 dB SNR. We obtain these results with only 600 unique training keyword samples. The training method is independent of the frontend and acoustic model topology.
Compact Speaker Embedding
(2020)
Deep neural networks (DNN) have recently been widely used in speaker recognition systems, achieving state-of-the-art performance on various benchmarks. The x-vector architecture is especially popular in this research community, due to its excellent performance and manageable computational complexity. In this paper, we present the lrx-vector system, which is the low-rank factorized version of the x-vector embedding network. The primary objective of this topology is to further reduce the memory requirement of the speaker recognition system. We discuss the deployment of knowledge distillation for training the lrx-vector system and compare against low-rank factorization with SVD. On the VOiCES 2019 far-field corpus we were able to reduce the weights by 28% compared to the full-rank x-vector system while keeping the recognition rate constant (1.83% EER).
Fiber lasers are increasingly replacing the commonly used diode lasers in laser transmission welding of plastics for challenging applications due to their superior beam quality and the ability to use long working distances and small spot sizes. At the same time, these smaller spot sizes increase the risk of thermal degradation. In this work, we investigate different scanning strategies that allow controlling the weld seam widths. These strategies are characterized regarding the demands on the performance of the scanner system, the risk of thermal degradation and the mechanical properties of the resulting welds.
With the growing global interdependence of companies, their scope of responsibility for the environmental, social, and human rights impacts associated with their activities is also growing. In this context, companies are increasingly held accountable for social and ecological issues that lie within the sphere of action of their suppliers and sub-suppliers. They are thus faced with the challenge of meeting these demands for transparency, traceability, and compliance with standards in their Supply Chains (SCs). Based on the theoretical framework of Sustainable Supply Chain Transparency (SSCT) in Sustainable Supply Chain Management (SSCM), this conceptual article aims at initiating the discussion on digitalization in SSCM. Therefore, a heuristical research framework, based on relevant fields in the management of sustainability oriented transparency (governance, cooperation and partner selection, traceability/tracking, and strategic and operational risk assessment) is developed. In relation to these fields, consequently, data-driven digital approaches are identified to which potentials for SSCT and control can be attributed. This initial analysis of existing digital approaches already shows that the market is developing dynamically, but is driven more by individual initiatives. In many cases, the approaches used so far are still in the trial phase or offer only limited solutions. Therefore, this paper contributes by giving an overview of the current application of the digitalization approaches in SSCM pinning our discussion on SSCT dimensions.
The objective of this study was to investigate the current research practices and existing progress research published on supply chain management and supply chain integration in an attempt to provide a research agenda for future investigations. Mainly the secondary data analysis and methods employed for the investigations. Particularly a systematic literature review analysis from 115 papers published from 16 important journals in the supply chain, logistics, Industrial management & data systems, supply chain management: an international journal and operations management during the period 1997 to 2018 was employed. This paper has identified relevant supply chain integration research studies that have contributed to the development and growth of manufacturing industries performance and competitiveness to the SCI and SCM fields. The analysis of the relevant articles gaps and miss-concepts in the previous literature provides to reduce universality and insufficient ordinary view, confusion of supply chain integrations were investigated. The gaps and investigations were done based on their methodological porches, objectives, analysis and research finding. Mainly the gaps, miss concepts and challenges concepts in the previous literature were identified and grouped in five issues. Moreover, the gaps and recommendations for future research are consolidated into a research agenda that provides practitioners to evaluate existing SCI & SCM issues and seek ways to develop a conceptual farm work on SCI of manufacturing industries to global supply chains.
2.0 μm fiber lasers provide a high beam quality and a high‐power output, which makes them ideal for welding and cutting a wide variety of commercially used plastics, as well as marking plastics, metals or even food. At 2.0 μm wavelength the intrinsic absorption of most thermoplastics is high enough to weld or cut without applying any absorbent additives or coatings. Compared to commonly used near‐infrared systems, welding with 2.0 μm lasers can improve heat distribution and gap bridging and enable transmission welding for challenging materials.
Password-hardened encryption (PHE) was introduced by Lai et al. at USENIX 2018 and immediately productized by VirgilSecurity. PHE is a password-based key derivation protocol that involves an oblivious external crypto service for key derivation. The security of PHE protects against offline brute-force attacks, even when the attacker is given the entire database. Furthermore, the crypto service neither learns the derived key nor the password. PHE supports key-rotation meaning that both the server and crypto service can update their keys without involving the user. While PHE significantly strengthens data security, it introduces a single point of failure because key-derivation always requires access to the crypto service. In this work, we address this issue and simultaneously increase security by introducing threshold password-hardened encryption. Our formalization of this primitive revealed shortcomings of the original PHE definition that we also address in this work. Following the spirit of prior works, we give a simple and efficient construction using lightweight tools only. We also implement our construction and evaluate its efficiency. Our experiments confirm the practical efficiency of our scheme and show that it is more efficient than common memory-hard functions, such as scrypt. From a practical perspective this means that threshold PHE can be used as an alternative to scrypt for password protection and key-derivation, offering better security in terms of offline brute force attacks. CCS CONCEPTS • Security and privacy → Cryptography.
In this paper, a flux linkage-based direct model predictive current control approach is presented for small permanent magnet synchronous motor (PMSM) drives. The method aims to minimize the current ripples at steady state by deciding on the optimal switching instant, while exhibiting fast dynamic behavior during transients. To this end, the future trajectory of the stator current is not computed based on the machine inductances or inductance look-up tables, but on the changes of the magnetic flux linkage by utilizing flux linkage maps. As shown, the proposed method can be particularly advantageous for electric drives with a noticeable nonlinearity in terms of saturation and/or cross-coupling effects since it allows for a significantly increased prediction accuracy, which leads to an improved steady-state performance as indicated by the reduced current distortions.
A transformer with variable primary windings for wide input voltage range DC/DC converters is presented. This transformer structure allows three states of the primary winding to be converted by only two individual windings. Based on the evaluations of a two-switch flyback converter, the potential of DC/DC converters with variable structures, the implementation of such a transformer and the advantages and disadvantages are shown and discussed. Real measurement results demonstrate the noticeable increase in efficiency and the reduction in component stress, having a positive influence on the operation of the converter and the component selection during the design process.
According to the current state of the art, adhesively bonded joints cannot be tested completely non-destructively. This is the main motivation to permanently monitor adhesively bonded joints in order to record their structural integrity and - as a result - to ensure safe load transmission. By the use of permanent non-destructive Structural Health Monitoring (SHM) methods, structural damages in the adhesive bond can be detected at an early stage. This paper presents a new method for monitoring the structural integrity of adhesively bonded joints by integrating a Polymer Optical Fiber (POF) into the adhesive layer. The sensor concept is based on a deformation transfer from the adhesive to the POF, which results in a change in the cross-sectional shape of the POF. The cross-sectional shape influences the optical light propagation in the POF, which can be detected by simple optical measuring devices. The paper describes the general principle and first results of investigations on a structural adhesive (3M Scotch-Weld DP 609). The sensor signal is in a good relation to the stress state of the adhesive layer.
We report on a combined quantitative charge carrier and catalytic activity analysis of Cu/ZnO(:Al) model catalysts. The promoting effect of Al3+ on the ZnO support for CO2 activation via the reverse water–gas-shift reaction has been investigated. The contact-free and operando microwave Hall Effect technique is applied to measure charge carriers in Cu/ZnO(:Al) based model catalysts under reverse water–gas shift reaction conditions. This method allows us to monitor the electrical conductivity, charge carrier mobility, and absolute number of charge carriers. An increase in charge carrier concentration with increasing Al3+ content and its direct correlation with the catalytic activity for CO formation is found. We conclude that the increased availability of charge carriers plays a key role in CO2 activation and CO formation, which finds additional support in a concurrent decrease of the apparent activation energy and increase in the reaction order of CO2. In combination with comprehensive DFT calculations, the impact of the interfacial charge transfer, coupled to oxygen defect sites in ZnO and CO2 adsorption properties, is elucidated and highlighted. In conclusion, the results from this operando investigation combined with DFT calculations demonstrate the importance of charge transfer processes as decisive descriptors for understanding and explaining catalytic properties.
Machine learning algorithms are increasingly used in industrial applications for a multitude of use-cases. However, using them in control tasks is a challenge due to real-time requirements and limited resources. In this paper, an implementation scheme for real-time inference of multilayer perceptron (MLP) neural networks on FPGAs is proposed. Design constraints for using MLPs in reinforcement learning agents for motor control applications are derived and accounted for in the implementation. Two MLP architectures are evaluated on an FPGA, and the timing and resource-usage data are reported. The real-time capability of the implementation for motor control applications is investigated for standard control frequencies. It is shown by experimental validation that real-time interference with an area-efficient implementation for motor control applications is achievable. Therefore, the proposed implementation scheme can be applied to deep reinforcement learning controllers with hard real-time requirements.
Ernährung als Medienthema
(2020)
Design of a Wide Input Voltage Range Current-Fed DC/DC Converter Within a Reduced Duty-Cycle Range
(2020)
A design of a current-fed push-pull converter for wide input voltage ranges within a reduced duty-cycle range is presented. Based on the converter conversion ratios of three different clamping configurations, the potential of the input clamping is shown analytical, simulative and by measurements. A differentiation is made between the four possible operating modes, which can be distinguished in the control of the semiconductors and the magnetomotive force in the reactor. By optimizing the turn ratios of both transformers, the wide input voltage range capability of the input clamped current-fed push-pull converter was improved, which has been proved by measurements on a real prototype.
According to the current state of the art, adhesively bonded joints cannot be tested completely non-destructively. This is the main motivation to permanently monitor adhesively bonded joints in order to record their structural integrity and - as a result - to ensure safe load transmission. By the us e of permanent non-destructive Structural Health Monitoring (SHM) methods, structural damages in the adhesive bond can be detected at an early stage. This paper presents a new method for monitoring the structural integrity of adhesively bonded joints by integrating a Polymer Optical Fiber (POF) into the adhesive layer. The sensor concept is based on a deformation transfer from the adhesive to the POF, which results in a change in the cross-sectional shape of the POF. The cross-sectional shape influences the optical light propagation in the POF, which can be detected by simple optical measuring devices. The paper describes the general principle and first results of investigations on a structural adhesive (3M Scotch-Weld DP 609). The sensor signal is in a good relation to the stress state of the adhesive layer.
Design Concept of a Wound Field Transverse Flux Machine using Soft Magnetic Composite Claw-Poles
(2020)
This paper presents the machine concept of a wound field transverse flux machine (WFTFM). In contrast to permanent magnet transverse flux machines (PMTFM), here the magneto motive force (mmf) of the rotor is generated by a current. The magnetic circuit of the WFTFM is made of soft magnetic composite (SMC) and is designed as claw-poles. Thus the magnetic circuit consists of only a few components. Together with the simple ring coils in stator and rotor, the WFTFM offers advantages in the production of electrical machines. The basic structure of the machine and a first design is described here. The results generated by numerical three-dimensional finite element analysis (FEA) are compared with those of conventional machines.
n this paper a semi-analytical calculation of a transverse flux machine is presented. First the design of the machine is explained, then the semi-analytical calculation, which is finally compared with a numerical 3D finite element analysis (FEA). The semi-analytical approach is based on a magnetic equivalent circuit, which is solved by nodal analysis and the Broyden-Method. The air gap of the machine is transformed into the magnetic equivalent circuit by conformal mapping using the Schwarz-Christoffel transformation. The comparison to numerical simulation shows that the approach gives good results and is sufficiently accurate for a rough design of the machine.
Enhanced Flexible Algorithm for the Optimization of Slot Filling Factors in Electrical Machines
(2020)
The continuous development in the field of industrial automation and electric mobility has led to the need for more efficient electrical machines with a high power density. The improvement of electrical machines’ slot filling factors is one of the measures to satisfy these requirements. In recent years, this topic has aroused greater interest in the industrial sector, since the evolution of the winding technological manufacturing processes allows an economically sustainable realization of ordered winding arrangements, rather than random ones. Moreover, the manufacture of electrical machines’ windings must be preceded by an accurate design phase in which it is possible to evaluate the maximum slot filling factor obtainable for a given wire shape and for its dimensions. For this purpose, this paper presents an algorithmic approach for the evaluation of maximum slot filling factors in electrical machines under an ideal geometric premise. In particular, this algorithm has a greater degree of flexibility with respect to the algorithm approaches found in the literature, since the study has been extended to round, rectangular and hexagonal wire sections. Furthermore, the slot filling factor calculation was carried out both for standard and non-standard slots. The algorithmic approach proposed can be considered as an additional useful tool for the fast design of electrical machine windings.
The Interior Permanent Magnet Synchronous machine (IPMSM) conventional mathematical model is generally employed to investigate and simulate the IPMSM
control and drive system behaviour. However, magnetic nonlinearities and spatial harmonics have a substantial influence on the IPMSM electromagnetic behaviour and
performances. In order to simulate the IPMSM real electromagnetic behaviour, this paper describes an enhanced mathematical model that takes into account the
saturation, cross-coupling and spatial harmonics effects. This model has been implemented in Matlab®/Simulink environment where the electric and magnetic parameters are derived from FEA investigations and implemented by the use of lookup tables. The high fidelity of the proposed IPMSM Simulink model is validated by the results of the FEA investigations carried out.
Development of a Two In One Motor System as a new Vehicle Energy Concept for Next Generation Car
(2020)
High Resolution Global NOx Sub-Model for Embedded System Application with Low Calibration Effort
(2020)
In this work a quasi-dimensional multi-zone combustion diagnostic tool for homogeneous charge Spark Ignition (SI) engines is analytically developed for the evaluation of heat release, flame propagation, combustion velocities as well as engine-out NOx and CO emissions, based on in-cylinder pressure data analysis. The tool can be used to assess the effects of fuel, design and operating parameters on the SI engine combustion and NOx and CO emissions formation processes. "Certain novel features are included in the presently developed combustion diagnostic tool. Firstly, combustion chambers of any shape and spark plug position can be considered due to an advanced model for the calculation of the geometric interaction between a spherically expanding flame and a general combustion chamber geometry. Also, the temperature stratification of the burned gas developed during the combustion phase, which has to be captured for the theoretically realistic estimation of the in-cylinder formation of pollutant emissions, is taken into account by a multi-zone thermodynamic treatment. According to this, multiple spherically allocated burned zones are sequentially generated at specified (user-defined) crank angle intervals, forming overlapping shells. Moreover, complex chemical equilibrium compositions are computed, which can include any user-defined mixture of species in the combustion products, using an advanced modular method based on the minimization of Gibbs energy. Furthermore, NOx and CO engine-out emissions are calculated based on novel in-cylinder formation models presented by the authors in the past. Specifically, NOx emissions are evaluated by a global algebraic NOx emissions formation model, accounting for both thermal and subO pathway NO formation, based on the calculation of the characteristic timescales of the relevant NO formation mechanisms. CO emissions are assessed using a new kinetics-based model, consisting of a single Ordinary Differential Equation (ODE) that can be analytically integrated. The CO emissions model is derived considering the dynamics of a representative pool of active radicals in post-flame gases and explicitly describes the CO oxidation quenching process. "The combustion diagnostic tool is applied to a lean burn gas engine at various engine speeds under full load conditions and constant lambda value. Various combustion and NOx and CO emissions related results are presented and discussed to illustrate the capabilities of the tool for combustion diagnosis, while at the same time calculated engine-out NOx and CO emissions are found to be in satisfactory agreement with measured ones.
Lastenräder sind im Stadtverkehr noch eine Marktnische, sollen aber künftig fester Bestandteil der „Verkehrswende“ sein. So formulierte der Bundesverkehrsminister auf dem Nationalen Radverkehrskongress in Dresden im Mai 2019 als drittes Leitziel für den nächsten Nationalen Radverkehrsplan: „Urbane Lastenverkehre werden Radverkehre“ (BMVI, 2019). Das Verlagerungspotential im urbanen Wirtschaftsverkehr wird mit 20 % beziffert (Gruber & Rudolph 2016), in der Kurier-, Express- und Paket-Branche (KEP) sogar mit 30 % (Bogdanski et al., 2018). Zur Zielerreichung müssen drei kritische Erfolgsfaktoren berücksichtigt werden – wirtschaftliche Logistikkonzepte, logistikgerechte Lastenräder und eine dazu passende urbane Verkehrsinfrastruktur. In diesem Spannungsfeld ergibt sich eine Vielzahl von Handlungsnotwendigkeiten. Weiterhin bestehen urbane Lastenverkehre nur zu einem Teil aus KEP-Verkehren. Welche Potentiale schwere Lastenräder für andere Anwendungen im Lastenverkehr bieten, erörtert das Forschungsprojekt „PedeListics“ mit Kommunen und Unternehmen als Praxispartnern.
<div class="section abstract"<div class="htmlview paragraph"In this work a quasi-dimensional multi-zone combustion diagnostic tool for homogeneous charge Spark Ignition (SI) engines is analytically developed for the evaluation of heat release, flame propagation, combustion velocities as well as engine-out NOx and CO emissions, based on in-cylinder pressure data analysis. The tool can be used to assess the effects of fuel, design and operating parameters on the SI engine combustion and NOx and CO emissions formation processes.</div<div class="htmlview paragraph"Certain novel features are included in the presently developed combustion diagnostic tool. Firstly, combustion chambers of any shape and spark plug position can be considered due to an advanced model for the calculation of the geometric interaction between a spherically expanding flame and a general combustion chamber geometry. Also, the temperature stratification of the burned gas developed during the combustion phase, which has to be captured for the theoretically realistic estimation of the in-cylinder formation of pollutant emissions, is taken into account by a multi-zone thermodynamic treatment. According to this, multiple spherically allocated burned zones are sequentially generated at specified (user-defined) crank angle intervals, forming overlapping shells. Moreover, complex chemical equilibrium compositions are computed, which can include any user-defined mixture of species in the combustion products, using an advanced modular method based on the minimization of Gibbs energy. Furthermore, NOx and CO engine-out emissions are calculated based on novel in-cylinder formation models presented by the authors in the past. Specifically, NOx emissions are evaluated by a global algebraic NOx emissions formation model, accounting for both thermal and N<sub2</subO pathway NO formation, based on the calculation of the characteristic timescales of the relevant NO formation mechanisms. CO emissions are assessed using a new kinetics-based model, consisting of a single Ordinary Differential Equation (ODE) that can be analytically integrated. The CO emissions model is derived considering the dynamics of a representative pool of active radicals in post-flame gases and explicitly describes the CO oxidation quenching process.</div<div class="htmlview paragraph"The combustion diagnostic tool is applied to a lean burn gas engine at various engine speeds under full load conditions and constant lambda value. Various combustion and NOx and CO emissions related results are presented and discussed to illustrate the capabilities of the tool for combustion diagnosis, while at the same time calculated engine-out NOx and CO emissions are found to be in satisfactory agreement with measured ones.</div</div
High Resolution Global NOx Sub-Model for Embedded System Application with Low Calibration Effort
(2020)
The starting point of the present work is a global model of NOx formation for stoichiometric and lean combustion of hydrocarbons developed on the basis of a single non-linear algebraic equation. The latter is the exact solution of a system of differential equations describing the main kinetic reaction schemes of NOx formation, because it’s been analytically derived. The NOx sub-model incorporates the well-established thermal (extended Zeldovich) and the N2O reaction paths, which are considered to be the most relevant NOx production paths under certain operating conditions in arbitrary engine application. Furthermore, the NOx sub-model proposed here relies on well-established and adopted mechanisms like the GRI-Mech 3.0 [25] and consequently requires no parameter adjustment.
The single equation NOx sub-model has been developed by the authors in a previous study [14] and shown satisfactory results when validated against test bench data of two different engines operated under stoichiometric and lean burn combustion conditions respectively. Therefore, there is a strong evidence, that its implementation on embedded systems for "in-situ" and "in memory" analysis of engine process data, or even its application as a virtual sensor, is of great importance. Unfortunately, the previous developed NOx sub-model requires a few seconds running time per engine cycle. This long running time makes the model though less attractive for a real-time application. In the current study the main goal is to drastically reduce the computational times without compromising robustness and accuracy. The require-ments for the time resolution on a dSpace Microautobox (MAB) is set to at least 1 kHz, meaning running times of the NOx sub-model of 1 ms per engine cycle, while its accuracy needs to be ensured at the levels of the detailed NOx sub-model as validated in [14].
The computationally most expensive steps have been identified and concern on the one hand the chemical equilibrium calculations based on the minimization of the free Gibbs energy and on the other hand the iterative solution method of the non-linear algebraic equation for the determination of the actual NOx concentration. Approaches for both steps have been developed and tested on the dSpace MAB II leading to an average computational time of 20 μs per point. Main focus of the present work is on the NOx sub-model and not the associated thermodynamic sub-models needed to describe the whole process. In order to cope with any uncertainties in NOx sub-model’s input parameters (p, T and mixture composition) only one calibration factor has been introduced and hence leads to low calibration effort. The final NOx sub-model results are compared to the detailed model and show a very good agreement.
Modeling the evolution of bone density is relevant for understanding, simulation and possible prediction of bone response to external and internal influences. In this work we present a formulation for the bone density evolution process that takes into account not only the commonly considered mechanical stimulus, but, as novelty, also the influence of the availability of nutrients and hormones, with its implementation pursued within the finite element method. A simple uni-axial extension test is used to illustrate and compare our novel model against the classical approach. The results of the proposed modified model are promising for application to real-life problems.
A number of previous studies have investigated the possibilities of modelling the change in density of bones. Remodeling can be formulated at the constitutive or the kinematic level. In this work we introduce a formulation for the density growth process which takes not only the mechanical stimulus into account but also the influence of age on the evolution of growth. We demonstrate the implementation in the context of the finite element method. This novel approach is illustrated for a simple uniaxial extension test and is verified against previous numerical results. Moreover, two further physiologically motivated examples are performed. The results of the proposed modified model show excellent agreement with comparable results from literature and are promising for the application to real-life problems.
BikeVR
(2020)
While becoming more and more aware of the ongoing climate change, eco-friendly means of transport for all citizens are moving further into focus. In order to be able to implement specific measures, it is necessary to better understand and emphasize sustainable transportation like walking and cycling through focused research. When developing novel traffic concepts and urban spaces for non-motorized traffic participants like bicycles and pedestrians, traffic and urban planning must be focused on their needs. To provide rare qualitative factors (such as stress, the perception of time and attractiveness of the environment) in this context, we present an audiovisual VR bicycle simulator which allows the user to cycle through a virtual urban environment by physically pedaling and also steering. Virtual Reality (VR) is a suitable tool in this context, as study participants find identical and almost freely definable (virtual) urban spaces with adjustable traffic scenarios. Our preliminary prototype proved to be promising and will be further optimized and evaluated.
Autonome mobile Roboter sind ein wesentlicher Bestandteil des Zukunftsprojekts Industrie4.0. Sie ermöglichen die ganzheitliche Automatisierung und Digitalisierung von Lieferketten, finden aber auch zunehmend im Bereich der Produktion ihre Einsatzgebiete. Ein konkreter Anwendungsfall, die Optimierung der Materialzuführung an Bestücklinien, wird im Rahmen dieser Masterarbeit behandelt. Im Wesentlichen geht es dabei um eine intelligente Just-in-Time-Anlieferung der benötigten SMD-Bauteilrollen mittels mobiler Roboter am Point-of-Use.
Dazu wird ein detailliertes Konzept namens „Auto Supply Tower“ erarbeitet, zwei Roboter-Prototypen aufgebaut und eine zentrale Managementsoftware entwickelt. Mithilfe eines erfolgreich durchgeführten Proof-of-Concepts kann sowohl die technische Machbarkeit, als auch die wirtschaftliche Rentabilität nachgewiesen werden. Die Robotik-Anwendung wird dabei auch den hohen Ansprüchen der Automobilelektronik-Industrie gerecht. Dies betrifft sowohl die Sicherheits- und ESD-Technik, als auch die Prozessstabilität der Anwendung. Insgesamt steckt in der intelligenten Automatisierung der Materialzuführung großes Optimierungspotential, wodurch unnötige manuelle Tätigkeiten vermieden und beachtliche Personalkosten eingespart werden können.
Entwicklungspsychologie
(2020)
Das Ziel der Bachelorarbeit ist die Konzeption einer Email Service Web Applikation. Das Kern Feature ist die Visualisierung der Emails in einer Tagcloud Struktur. Dazu wird Google’s Gmail API verwendet. Ein Node.js Server ist für das aufbereiten der Daten zuständig. Als dritter Akteur wird ein React Frontend verwendet um die aufbereiteten Daten weiter zu benutzen. Zur Datendarstellung wird die d3.js Library verwendet. Diese Technologien bilden das Grundgerüst der Applikation. Ziel ist es mit der neuen Visualisierung der Emails eine neue Perspektive der Email Inbox zu erhalten und Interaktion mit Datenabbildung zu verbinden.
Die Performance der Kontaktverfolgung, Isolation und Quarantäne bleibt deutlich hinter den Möglichkeiten zurück und trägt zu wenig zur Reduktion der Reproduktionszahlen bei. In Kombination mit einer nachlassenden Mitwirkung der Bevölkerung könnte bald ein Punkt erreicht sein, an dem wir nur noch auf die Impfung warten können. Wir müssen schnellstmöglich auf ein Niveau der Neuinfektionen zurückzukehren, auf dem die Gesundheitsämter einen Beitrag leisten können. Wir brauchen eine Stelle, die für durchgängig (!) pfeilschnelle (!) Abläufe im Gesamtprozess (Test-Zugang, schnellste Auswertung und Mitteilung von Testergebnissen, umgehende Isolierung, Kontaktverfolgung, Testung der Kontakte sowie wirksame Quarantäne.) verantwortlich zeichnet. Dabei sollten Kompetenzen zu Management, Organisation, zeitlichen Abläufen, 24/7-Betrieb, Drive-In-Testing, detektivischer Arbeit, IT-Einsatz und mathematisch basierter Optimierung des Gesamtprozesses im Vordergrund stehen.
Anders lehren und lernen
(2020)
Potential- und Speicheranalyse im Bereich regenerative Energien in Oberfranken (Abschlussbericht)
(2020)
Im Folgenden werden die Fragestellungen aufgeführt, welche sich aus dem Ablauf ergeben und welche im Laufe des Projekts geklärt werden sollen:
1) Wie viel Strom wird heute im Untersuchungsgebiet verbraucht (Zuhilfenahme der Bachelorarbeit von Fr. Katharina Will)?
2) Wie viele Windkraft-, Wasserkraft-, Bio-/ Klärgas-, Photovoltaik- und Speicheranlagen bestehen bereits heute und wie viel Strom wird dadurch erzeugt und speichertechnisch umgesetzt?
3) Welche Jahre von 1980 bis 2019 sind die wettertechnisch ungünstigsten für die Nutzung von Windkraft und Photovoltaik?
4) Wie viel Strom muss noch zusätzlich aus Sonnen- und Windkraft erzeugt werden, um den gegenwärtigen Strombedarf aus reinen regenerativen Quellen zu decken? Wie kann diese regenerative Energieversorgung, unter Berücksichtigung der geringsten Stromgestehungskosten und der wettertechnisch ungünstigsten Zeiträume, zu den bereits bestehenden Energiespeichern ausgelegt werden?
5) Wie ist der aktuelle Stand der Technik bzgl. Photovoltaik und wie hoch sind die Kosten für solche Anlagen?
Im Vortragsverlauf werden die Fragestellungen aufgeführt, welche im Laufe des Projekts geklärt wurden:
1) Wie viel Strom wird heute im Untersuchungsgebiet verbraucht (Zuhilfenahme der Bachelorarbeit von Fr. Katharina Will)?
2) Wie viele Windkraft-, Wasserkraft-, Bio-/ Klärgas-, Photovoltaik- und Speicheranlagen bestehen bereits heute und wie viel Strom wird dadurch erzeugt und speichertechnisch umgesetzt?
3) Welche Jahre von 1980 bis 2019 sind die wettertechnisch ungünstigsten für die Nutzung von Windkraft und Photovoltaik?
4) Wie viel Strom muss noch zusätzlich aus Sonnen- und Windkraft erzeugt werden, um den gegenwärtigen Strombedarf aus reinen regenerativen Quellen zu decken? Wie kann diese regenerative Energieversorgung, unter Berücksichtigung der geringsten Stromgestehungskosten und der wettertechnisch ungünstigsten Zeiträume, zu den bereits bestehenden Energiespeichern ausgelegt werden?
5) Wie ist der aktuelle Stand der Technik bzgl. Photovoltaik und wie hoch sind die Kosten für solche Anlagen?
Analyse des besten Stromversorgungsmix des Untersuchungsgebietes um Ansbach durch reine regenerative Stromversorgung, anhand des ungünstigsten Wetterjahres der letzten 40 Jahre mit Vertiefung der aktuellen Technik der Wasserkraft in der gesamten Metropolregion Nürnberg wie auch der Kostenfaktoren für Laufwasserkraftwerke
Das Projekt wird im Rahmen des Seminars „Projekt Energietechnik“ des sechsten Semesters Maschinenbau an der Technischen Hochschule Nürnberg durchgeführt. Die Projektarbeit untersucht die Möglichkeiten einer autarken regenerativen Energieversorgung in einer mittelfränkischen Region. Hierbei wird einerseits das Energieerzeugungspotential der Energieträger Wind, Sonne, Biomasse und Laufwasser und andererseits der Verbrauch des Untersuchungsgebiets analysiert. Bei der Gegenüberstellung der Jahreswerte für die Energieerzeugung und des
Energieverbrauchs von 1980 bis 2019 fällt der volatile Verlauf der Zeitreihen auf. Um die Überschüsse in der Erzeugung besser ausnutzen zu können und gleichzeitig die Defizite decken zu können, werden mehrere Speichertechnologien verglichen. Mit Hilfe eines Excel-Tools von Herrn Prof. Dr. Popp werden Daten der letzten 40 Jahre analysiert und verrechnet. Dabei werden die Jahre mit extremen Energiedefiziten herausgefunden, in denen die größten Speicherleerungen stattfinden.
Daraus kann man schließen, dass die Untersuchungsregion Speicher-technologien für die Deckung der Energiedefizite vorhalten muss, um die benötigte Energiemenge für den Verbrauch bereitzustellen. Des Weiteren wird mit Hilfe des Programms ein optimaler Mix der Erneuerbaren Energieträger berechnet. Zudem lassen sich für deren Ausbau kostenminimale Lösungen errechnen. Im Folgenden lassen sich konkrete Werte für den zahlenmäßigen Ausbau der Anlagen geben.
Außerdem behandelt diese Projektarbeit die Speichertechnologie Power-to-Gas-toPower ausführlicher. Es sollen das Prinzip, die Funktions-weise, die Verfahren und die Kosten dargestellt werden. Die Behandlung der Vor- und Nachteile und die Entwicklung in der Zukunft werden abschließend betrachtet.
Das Projekt wird im Rahmen des Seminars „Projekt Energietechnik“ des sechsten Semesters Maschinenbau an der Technischen Hochschule Nürnberg durchgeführt. Die Projektarbeit untersucht die Möglichkeiten einer autarken regenerativen Energieversorgung in einer mittelfränkischen Region. Hierbei wird einerseits das Energieerzeugungspotential der Energieträger Wind, Sonne, Biomasse und Laufwasser und andererseits der Verbrauch des Untersuchungsgebiets analysiert. Bei der Gegenüberstellung der Jahreswerte für die Energieerzeugung und des
Energieverbrauchs von 1980 bis 2019 fällt der volatile Verlauf der Zeitreihen auf. Um die Überschüsse in der Erzeugung besser ausnutzen zu können und gleichzeitig die Defizite decken zu können, werden mehrere Speichertechnologien verglichen. Mit Hilfe eines Excel-Tools von Herrn Prof. Dr. Popp werden Daten der letzten 40 Jahre analysiert und verrechnet. Dabei werden die Jahre mit extremen Energiedefiziten herausgefunden, in denen die größten Speicherleerungen stattfinden.
Daraus kann man schließen, dass die Untersuchungsregion Speicher-technologien für die Deckung der Energiedefizite vorhalten muss, um die benötigte Energiemenge für den Verbrauch bereitzustellen. Des Weiteren wird mit Hilfe des Programms ein optimaler Mix der Erneuerbaren Energieträger berechnet. Zudem lassen sich für deren Ausbau kostenminimale Lösungen errechnen. Im Folgenden lassen sich konkrete Werte für den zahlenmäßigen Ausbau der Anlagen geben.
Außerdem behandelt diese Projektarbeit die Speichertechnologie Power-to-Gas-toPower ausführlicher. Es sollen das Prinzip, die Funktions-weise, die Verfahren und die Kosten dargestellt werden. Die Behandlung der Vor- und Nachteile und die Entwicklung in der Zukunft werden abschließend betrachtet.
Die NASA stellt mit ihrer Reanalyse-Datenbank MERRA-2 weltweit aufgezeichnete Klimadaten wie Sonneneinstrahlung, Windgeschwindigkeiten etc. zur Verfügung. Die Daten sind jeweils einem Rastergebiet zugeordnet, welche durch ein weltumspannendes Raster mit einer Auflösung von ca. 0,5° x 0,625° Breitengrad x Längengrad ent-steht. Es liegen aktuell Messdaten aus den Jahren 1980 bis 2019 vor (NASA, 2017, S. 1-3). In dieser Ausarbeitung werden diese Rohdaten aufbereitet, analysiert und hinsichtlich ihrer Aussagekraft für eine zukünftige Nutzung der vorhandenen regenerativen Energieformen zur Umwandlung in Nutzenergie interpretiert. Es wird erörtert, welche regenerativen Energieträger vor allem auch in Verbindung mit Speichertechnologien zur rein regenerativen Deckung des Energiebedarfs geeignet sind. Im Vergleich mit dem aktuellen Bestand an regenerativen Energieanlagen und unter der Annahme von gleichbleibender Einspeiseleistung aus Biomasse- und Laufwasseranlagen wird eine Empfehlung für einen optimalen Energiemix gegeben. Die Analyse bezieht sich auf ein Teilgebiet im Süden der Europäischen Metropolregion Nürnberg. Ein weiterer Bestandteil der Arbeit ist die Recherche zum aktuellen Technologiestand und den Kosten von geotechnischen Speichersystemen.
E-Learning an Hochschulen
(2020)
Zwei Jahrzehnte E-Learning haben die Hochschulen der viel zitierten und oft beschworenen "Bildungsrevolution" nicht entscheidend nahegebracht. Der Arbeitsbericht bietet vor dem Hintergrund dieser Erkenntnis zunächst einen historischen Abriss der Entwicklung des Themas sowie eine ordnende Bestandsaufnahme heute etablierter, wenn auch nach wie vor nicht in der Fläche verfügbarer Konzepte und Realisierungsansätze. Sodann werden die Potenziale des E-Learnings im Kontext der Hochschullehre beleuchtet, wobei neben bekannten Formaten insbesondere denkbare Entwicklungslinien diskutiert werden, in denen innovative Bausteine vordergründig lernfremder Handlungsfelder der Digitalisierung das E-Learning befruchten und zukunftsorientierte Szenarien prägen könnten. Abschließend setzt sich der Bericht mit den zentralen Problemen auseinander, die einen nachhaltigen Durchbruch des Themas E-Learning an Hochschulen bis dato verhindert haben und nach wie vor hemmen. Zusammenfassung und Ausblick erfolgen in Form von zwölf Thesen.