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A new yield function for lamellar gray cast iron materials is proposed. The new model is able to describe the results of recently performed microstructure-based finite-element computations that resolve the three dimensional yield surface of three different gray cast irons. The yield function requires only the yield stress in tension and compression of the respective material as model parameters. Furthermore, the algorithmic formulation of the new model is assessed for numerical robustness and efficiency.
Das vorliegende Buch richtet sich vorwiegend an Webentwickler und Webinteressierte. Zunächst wird ein Überblick über Digital Storytelling, seine Geschichte und die verschiedenen heutigen Ausprägungen gegeben. Dabei liegt der Fokus auf Digital Storytelling im Web auf Basis von Animation.
Besonders die Idee der scroll-activated animation als zeitgemäßes Konzept für das Storytelling wird in diesem Buch in den Mittelpunkt gestellt.
Als technische Möglichkeiten zur Umsetzung von Webanimationen werden CSS3, SVG und HTML5-Canvas vorgestellt und diese in der Folge detailliert beschrieben.
Im Rahmen eines umfangreich dokumentierten Demoprojekts wird intensiv auf die Konzeption einer scroll-actived animation basierten Website und deren technische Umsetzung mittels HTML5, CSS3, JavaScript/jQuery und ScrollMagic sowie die damit verbundenen Herausforderungen eingegangen. Dies erlaubt dem Leser das detaillierte Nachvollziehen der Erstellung und die davon abgeleitete Entwicklung eigener Projekte.
Der Inhalt des Buches
- Definition, Entwicklung und psychologische Wirkungsweisen
- Medienformen
- Digital Storytelling
- Animationen im Web
- Technische Umsetzung
Die Zielgruppen des Buches
- Konzepter
- Webentwickler
- Mediengestalter
- Studenten, Azubis
- Webinteressierte
Die Industrie wird mehr denn je konfrontiert mit verkürzten Durchlaufzeiten, zunehmenden Forderungen im Hinblick auf die Produkthaftung und steigenden Ansprüchen an das Qualitäts-Management wie z.B. DIN/ISO 9001. Hinzu kommen Faktoren wie die Globalisierung. Internationale Kooperationen führen zu einer parallelen und koordinierten Zusammenarbeit verschiedener Einheiten eines Unternehmens einerseits und verschiedener Unternehmen andererseits. Ein probater Ansatz, diesen Herausforderungen mit Hilfe von Softwarelösungen gerecht zu werden, verbirgt sich hinter dem Kürzel PLM (Product-Lifecycle-Management). PLM soll ermöglichen, die Produkte schneller und besser zur Marktreife zu bringen, indem allgegenwärtig Informationen zu Produkt und Produktion bereitstehen, Entscheidungen und Abläufe besser abgesichert und zudem die Kosten gesenkt werden können. PLM umfasst das Cross Enterprise Engineering als Nachfolger von Simultaneous Engineering und Concurrent Design sowie das Management der auf das Produkt bezogenen geistigen Eigentumsrechte. Beide Methoden basieren darauf, die Engineering-Tätigkeiten über den gesamten Produktlebenszyklus organisatorisch und systemtechnisch zu unterstützen. PLM reiht sich zusammen mit CRM (Customer-Relationship-Management), SCM (Supply-Chain-Management), MRP (Material Resource Planning) und ERM (Enterprise-Resource-Management) in die strategischen Backbones eines Unternehmens ein. Um PLM zu verstehen, muss man sich zunächst mit den Funktionen eines Systems zum Produktdaten-Management (PDM) befassen: Diese beinhalten im Wesentlichen die Verwaltung von Produktdaten und Konstruktionsprozessen, die Gruppentechnik und Klassifikation, das Ein- und Ausgabe-Management sowie die Integration mit den Erzeugersystemen (CAD-M, CAD-E, Office etc.) und mit der Produktplanung und -steuerung (PPS). PLM-Systeme sind evolutionär aus den existierenden PDM-Systemen entstanden, enthalten immer deren gesamte PDM-Funktionen als Basis. PLM integriert darüber hinaus alle produktrelevanten Informationen und Prozesse innerhalb eines Unternehmens über verschiedene Standorte hinweg und außerhalb mit Zulieferern, Partnern und Kunden, wobei hierfür das Internet eine wesentliche Rolle spielt.
The following contribution deals with the experimental investigation and theoretical evaluation of fatigue crack growth under isothermal and non-isothermal conditions at the nickel alloy 617. The microstructure and mechanical properties of alloy 617 are influenced significantly by the thermal heat treatment and the following thermal exposure in service. Hence, a solution annealed and a long-time service exposed material condition is studied. The crack growth measurement is carried out by using an alternate current potential drop system, which is integrated into a thermomechanical fatigue (TMF) test facility. The measured fatigue crack growth rates results in a function of material condition, temperature and load waveform. Furthermore, the results of the non-isothermal tests depend on the phase between thermal and mechanical load (in-phase, out-of-phase). A fracture mechanic based, time dependent model is upgraded by an approach to consider environmental effects, where almost all model parameters represent directly measureable values. A consistent description of all results and a good correlation with the experimental data can be achieved.
Vorgestellt wird ein Konzept zur biologischen Methanisierung von Wasserstoff direkt in Biogasreaktoren, mit dem durch Membranbegasung der Methangehalt des Biogases auf > 96 % erhöht werden kann. Essentiell zum Erreichen solch hoher Methanwerte sind die Einhaltung eines optimalen pH-Bereichs und die Vermeidung von H2-Akkumulation. Im Falle einer Limitierung der Methanbildungsrate durch den eigentlichen anaeroben Abbauprozess der Biomasse ist auch eine externe Zufuhr von CO2 zur weiteren Methanbildung denkbar. Das Verfahren soll weiter optimiert und in einem von der Deutschen Bundesstiftung Umwelt geförderten Projekt in der Biogasanlage einer regionalen Käserei in der Praxis getestet werden. Die hier angestrebte Kombination aus dezentraler Abfallverwertung und Eigenenergieerzeugung eines lebensmittelverarbeitenden Betriebs unter Einbindung in ein intelligentes Erneuerbare Energien - Konzept soll einen zusätzlichen Mehrwert liefern.
In the area of cloud computing, judging the fulfillment of service-level agreements on a technical level is gaining more and more importance. To support this we introduce privacy preserving set relations as inclusiveness and disjointness based ao Bloom filters. We propose to compose them in a slightly different way by applying a keyed hash function. Besides discussing the correctness of set relations, we analyze how this impacts the privacy of the sets content as well as providing privacy on the sets cardinality. Indeed, our solution proposes to bring another layer of privacy on the sizes. We are in particular interested how the overlapping bits of a Bloom filter impact the privacy level of our approach. We concretely apply our solution to a use case of cloud security audit on access control and present our results with real-world parameters.
This work discusses several use cases of post-mortem mobile device tracking in which privacy is required e.g. due to client-confidentiality agreements and sensibility of data from government agencies as well as mobile telecommunication providers. We argue that our proposed Bloomfilter based privacy approach is a valuable technical building block for the arising General Data Protection Regulation (GDPR) requirements in this area. In short, we apply a solution based on the Bloom filters data structure that allows a 3rd party to performsome privacy saving setrelations on a mobiletelco’s access logfile or other mobile access logfile from harvesting parties without revealing any other mobile users in the proximity of a mobile base station but still allowing to track perpetrators.
Finding clusters in high dimensional data is a challenging research problem. Subspace clustering algorithms aim to find clusters in all possible subspaces of the dataset, where a subspace is a subset of dimensions of the data. But the exponential increase in the number of subspaces with the dimensionality of data renders most of the algorithms inefficient as well as ineffective. Moreover, these algorithms have ingrained data dependency in the clustering process, which means that parallelization becomes difficult and inefficient. SUBSCALE is a recent subspace clustering algorithm which is scalable with the dimensions and contains independent processing steps which can be exploited through parallelism. In this paper, we aim to leverage the computational power of widely available multi-core processors to improve the runtime performance of the SUBSCALE algorithm. The experimental evaluation shows linear speedup. Moreover, we develop an approach using graphics processing units (GPUs) for fine-grained data parallelism to accelerate the computation further. First tests of the GPU implementation show very promising results.
In many application domains, in particular automotives, guaranteeing a very low failure rate is crucial to meet functional and safety standards. Especially, reliable operation of memory components such as SRAM cells is of essential importance. Due to aggressive technology downscaling, process and runtime variations significantly impact manufacturing yield as well as functionality. For this reason, a thorough memory failure rate assessment is imperative for correct circuit operation and yield improvement. In this regard, Monte Carlo simulations have been used as the conventional method to estimate the variability induced failure rate of memory components. However, Monte Carlo methods become infeasible when estimating rare events such as high-sigma failure rates. To this end, Importance Sampling methods have been proposed which reduce the number of required simulations substantially. However, existing methods still suffer from inaccuracies and high computational efforts, in particular for high-sigma problems. In this paper, we fill this gap by presenting an efficient mixture Importance Sampling approach based on Bayesian optimization, which deploys a surface model of the objective function to find the most probable failure points. Its advantages include constant complexity independent of the dimensions of design space, the potential to find the global extrema, and higher trustworthiness of the estimated failure rate by accurately exploring the design space. The approach is evaluated on a 6T-SRAM cell as well as a master-slave latch based on a 28nm FDSOI process. The results show an improvement in accuracy, resulting in up to 63× better accuracy in estimating failure rates compared to the best state-of-the-art solutions on a 28nm technology node.
Oxide semiconductors have the potential to increase the performance of inkjet printed microelectronic devices such as field-effect transistors (FETs), due to their high electron mobilities. Typical metal oxides are n-type semiconductors, while p-type oxides, although realizable, exhibit lower carriermobilities. Therefore, the circuit design based on oxide semiconductors is mostly in n-type logic only. Here we present an inkjet printed pn-diode based on p- and n-type oxide semiconductors.Copper oxide or nickel oxide is used as p-typesemiconductor whereas n-typesemiconductor is realized with indium oxide. Themeasurements show that the pn-diodes operate in the voltage window typical for printed electronics and the emission coefficient is 1.505 and 2.199 for the copper oxide based and nickel oxidebased pn-diode, respectively.Furthermore, a pn-diode model is developed and integrable into a circuit simulator.
Printed systems spark immense interest in industry, and for several parts such as solar cells or radio frequency identification antennas, printed products are already available on the market. This has led to intense research; however, printed field-effect transistors (FETs) and logics derived thereof still have not been sufficiently developed to be adapted by industry. Among others, one of the reasons for this is the lack of control of the threshold voltage during production. In this work, we show an approach to adjust the threshold voltage (Vth) in printed electrolyte-gated FETs (EGFETs) with high accuracy by doping indium-oxide semiconducting channels with chromium. Despite high doping concentrations achieved by a wet chemical process during precursor ink preparation, good on/off-ratios of more than five orders of magnitude could be demonstrated. The synthesis process is simple, inexpensive, and easily scalable and leads to depletion-mode EGFETs, which are fully functional at operation potentials below 2 V and allows us to increase Vth by approximately 0.5 V.
Development of Fully Printed Oxide Field-Effect Transistors using Graphene Passive Structures
(2019)
During the past decade to the present time, the topic of printed electronics has gained a lot of attention for their potential use in a number of practical applications, including biosensors, photovoltaic devices, RFIDs, flexible displays, large-area circuits, and so on. To fully realize printed electronic components and devices, effective techniques for the printing of passive structures and electrically and chemically compatible materials in the printed devices need to be developed first. The opportunity of using electrically conducting graphene inks will enable the integration of passive structures into active devices, as for example, printed electrolyte-gated transistors (EGTs). Accordingly, in this study, we present the parametric results obtained on fully printed electrolyte-gated transistors having graphene as the passive electrodes, an inorganic oxide semiconductor as the active channel, and a composite solid polymer electrolyte (CSPE) as the gate insulating material. This configuration offers high chemical and electrical stability while at the same time allowing EGT operation at low potentials, implying the distinct advantage of operation at low input voltages. The printed in-plane EGTs we developed exhibit excellent performance with device mobility up to 16 cm2 V–1 s–1, an ION/IOFF ratio of 105, and a subthreshold slope of 120 mV dec–1.
Printed electronics (PE) circuits have several advantages over silicon counterparts for the applications where mechanical flexibility, extremely low-cost, large area, and custom fabrication are required. The custom (personalized) fabrication is a key feature of this technology, enabling customization per application, even in small quantities due to low-cost printing compared with lithography. However, the personalized and on-demand fabrication, the non-standard circuit design, and the limited number of printing layers with larger geometries compared with traditional silicon chip manufacturing open doors for new and unique reverse engineering (RE) schemes for this technology. In this paper, we present a robust RE methodology based on supervised machine learning, starting from image acquisition all the way to netlist extraction. The results show that the proposed RE methodology can reverse engineer the PE circuits with very limited manual effort and is robust against non-standard circuit design, customized layouts, and high variations resulting from the inherent properties of PE manufacturing processes.
Printed electronics (PE) is a fast growing technology with promising applications in wearables, smart sensors and smart cards since it provides mechanical flexibility, low-cost, on-demand and customizable fabrication. To secure the operation of these applications, True Random Number Generators (TRNGs) are required to generate unpredictable bits for cryptographic functions and padding. However, since the additive fabrication process of PE circuits results in high intrinsic variation due to the random dispersion of the printed inks on the substrate, constructing a printed TRNG is challenging. In this paper, we exploit the additive customizable fabrication feature of inkjet printing to design a TRNG based on electrolyte-gated field effect transistors (EGFETs). The proposed memory-based TRNG circuit can operate at low voltages (≤ 1 V ), it is hence suitable for low-power applications. We also propose a flow which tunes the printed resistors of the TRNG circuit to mitigate the overall process variation of the TRNG so that the generated bits are mostly based on the random noise in the circuit, providing a true random behaviour. The results show that the overall process variation of the TRNGs is mitigated by 110 times, and the simulated TRNGs pass the National Institute of Standards and Technology Statistical Test Suite.
Embedded Analog Physical Unclonable Function System to Extract Reliable and Unique Security Keys
(2020)
Internet of Things (IoT) enabled devices have become more and more pervasive in our everyday lives. Examples include wearables transmitting and processing personal data and smart labels interacting with customers. Due to the sensitive data involved, these devices need to be protected against attackers. In this context, hardware-based security primitives such as Physical Unclonable Functions (PUFs) provide a powerful solution to secure interconnected devices. The main benefit of PUFs, in combination with traditional cryptographic methods, is that security keys are derived from the random intrinsic variations of the underlying core circuit. In this work, we present a holistic analog-based PUF evaluation platform, enabling direct access to a scalable design that can be customized to fit the application requirements in terms of the number of required keys and bit width. The proposed platform covers the full software and hardware implementations and allows for tracing the PUF response generation from the digital level back to the internal analog voltages that are directly involved in the response generation procedure. Our analysis is based on 30 fabricated PUF cores that we evaluated in terms of PUF security metrics and bit errors for various temperatures and biases. With an average reliability of 99.20% and a uniqueness of 48.84%, the proposed system shows values close to ideal.
Die Erfindung betrifft eine Vorrichtung zur biologischen Methanisierung von Kohlenstoffdioxid mittels methanogener Mikroorganismen durch Umsetzung von Wasserstoff und Kohlenstoffdioxid die Folgendes aufweist: (a) einen Reaktor; (b) ein in dem Reaktor bereitgestelltes Medium mit methanogenen Mikroorganismen; (c) eine Zuführeinrichtung zum Zuführen eines H2-enthaltenden Gases in das Medium; wobei (d) die Zuführeinrichtung eine Mehrzahl an Gasführungseinheiten umfasst, wobei jede der Gasführungseinheiten eine Mehrzahl an Begasungseinheiten aufweist, die jeweils eine Vielzahl an Auslassöffnungen zum Zuführen des H2-enthaltenden Gases in das Medium aufweist, wobei die Zuführeinrichtung derart ausgestaltet ist, dass das Zuführen des H2-enthaltenden Gases durch sukzessive Beaufschlagung der Gasführungseinheiten erfolgen kann. Weiterhin betrifft die Erfindung auch ein Verfahren zur biologischen Methanisierung von Kohlenstoffdioxid in einer Reaktorvorrichtung mittels methanogener Mikroorganismen als Teil eines in einem Reaktor bereitgestellten Mediums, dadurch gekennzeichnet, dass ein H2-enthaltendes Gas über eine Mehrzahl an Gasführungseinheiten dem Medium zugeführt wird, wobei jede der Gasführungseinheiten eine Mehrzahl an Begasungseinheiten aufweist, die jeweils eine Vielzahl an Auslassöffnungen aufweisen, wobei das Zuführen des H2-enthaltenden Gases durch sukzessive Beaufschlagung der Gasführungseinheiten erfolgt. Dem H2-enthaltenden Medium kann nach Bedarf Kohlenstoffdioxid beigemischt werden.
Die Erfindung betrifft eine Photovoltaik-Stromversorgungsvorrichtung, insbesondere vom öffentlichen Stromnetz unabhängige Photovoltaik-Stromversorgungsvorrichtung, mit einem positiven (204) und einem negativen (206) Lastanschluss für den Anschluss einer elektrischen Last, mit einer Mehrzahl von photovoltaischen Zellen (104) und einer wiederaufladbaren Batterie (5), welche wenigstens zwei in Serie geschaltete Batteriezellen (112) umfasst. Nach der Erfindung sind die photovoltaischen Zellen (104) zu einer der Anzahl der Batteriezellen (112) entsprechenden Anzahl von seriell verbundenen Teilstrings (108) verschaltet und jeder Teilstring (108) ist mit einem positiven Teilstringanschluss mit einem Pluspol und mit einem negativen Teilstringanschluss mit einem Minuspol einer zugeordneten Batteriezelle (112) oder mehreren zugeordneten parallel geschalteten Batteriezellen (112) verbunden. Dabei kann jeder Teilstring (108) zwischen dem positiven und negativen Teilstringanschluss eine maximale Leerlaufspannung erzeugen, die kleiner oder gleich einer vorgegebenen Ladeschlussspannung der zugeordneten Batteriezelle (112) oder der zugeordneten parallel geschalteten Batteriezellen (112) ist. Weiterhin betrifft die Erfindung eine Schaltungsanordnung zum Laden einer wiederaufladbaren Batterie, die zur Realisierung einer derartigen Photovoltaik-Stromversorgungsvorrichtung geeignet ist.
Printed Electronics is perceived to have a major impact in the fields of smart sensors, Internet of Things and wearables. Especially low power printed technologies such as electrolyte gated field effect transistors (EGFETs) using solution-processed inorganic materials and inkjet printing are very promising in such application domains. In this paper, we discuss a modeling approach to describe the variations of printed devices. Incorporating these models and design flows into our previously developed printed design system allows for robust circuit design. Additionally, we propose a reliability-aware routing solution for printed electronics technology based on the technology constraints in printing crossovers. The proposed methodology was validated on multiple benchmark circuits and can be easily integrated with the design automation tools-set.
Printed electrolyte-gated oxide electronics is an emerging electronic technology in the low voltage regime (≤1 V). Whereas in the past mainly dielectrics have been used for gating the transistors, many recent approaches employ the advantages of solution processable, solid polymer electrolytes, or ion gels that provide high gate capacitances produced by a Helmholtz double layer, allowing for low-voltage operation. Herein, with special focus on work performed at KIT recent advances in building electronic circuits based on indium oxide, n-type electrolyte-gated field-effect transistors (EGFETs) are reviewed. When integrated into ring oscillator circuits a digital performance ranging from 250 Hz at 1 V up to 1 kHz is achieved. Sequential circuits such as memory cells are also demonstrated. More complex circuits are feasible but remain challenging also because of the high variability of the printed devices. However, the device inherent variability can be even exploited in security circuits such as physically unclonable functions (PUFs), which output a reliable and unique, device specific, digital response signal. As an overall advantage of the technology all the presented circuits can operate at very low supply voltages (0.6 V), which is crucial for low-power printed electronics applications.
Electrolyte-gated, printed field-effect transistors exhibit high charge carrier densities in the channel and thus high on-currents at low operating voltages, allowing for the low-power operation of such devices. This behavior is due to the high area-specific capacitance of the device, in which the electrolyte takes the role of the dielectric layer of classical architectures. In this paper, we investigate intrinsic double-layer capacitances of ink-jet printed electrolyte-gated inorganic field-effect transistors in both in-plane and top-gate architectures by means of voltage-dependent impedance spectroscopy. By comparison with deembedding structures, we separate the intrinsic properties of the double-layer capacitance at the transistor channel from parasitic effects and deduce accurate estimates for the double-layer capacitance based on an equivalent circuit fitting. Based on these results, we have performed simulations of the electrolyte cutoff frequency as a function of electrolyte and gate resistances, showing that the top-gate architecture has the potential to reach the kilohertz regime with proper optimization of materials and printing process. Our findings additionally enable accurate modeling of the frequency-dependent capacitance of electrolyte/ion gel-gated devices as required in the small-signal analysis in the circuit simulation.
In the domain of printed electronics (PE), field-effect transistors (FETs) with an oxide semiconductor channel are very promising. In particular, the use of high gate-capacitance of the composite solid polymer electrolytes (CSPEs) as a gate-insulator ensures extremely low voltage requirements. Besides high gate capacitance, such CSPEs are proven to be easily printable, stable in air over wide temperature ranges, and possess high ion conductivity. These CSPEs can be sensitive to moisture, especially for high surface-to-volume ratio printed thin films. In this paper, we provide a comprehensive experimental study on the effect of humidity on CSPE-gated single transistors. At the circuit level, the performance of ring oscillators (ROs) has been compared for various humidity conditions. The experimental results of the electrolyte-gated FETs (EGFETs) demonstrate rather comparable currents between 30%-90% humidity levels. However, the shifted transistor parameters lead to a significant performance change of the RO frequency behavior. The study in this paper shows the need of an impermeable encapsulation for the CSPE-gated FETs to ensure identical performance at all humidity conditions.
Die Wörz´sche Heuristik
(2019)
Radio frequency identification (RFID) antennas are popular for high frequency (HF) RFID, energy transfer and near field communication (NFC) applications. Particularly for wireless measurement systems the RFID/NFC technology is a good option to implement a wireless communication interface. In this context, the design of corresponding reader and transmitter antennas plays a major role for achieving suitable transmission quality. This work proves the feasibility of the rapid prototyping of a RFID/NFC antenna, which is used for the wireless communication and energy harvesting at the required frequency of 13.56 MHz. A novel and low-cost direct ink writing (DIW) technology utilizing highly viscous silver nanoparticle ink is used for this process. This paper describes the development and analysis of low-cost printed flexible RFID/NFC antennas on cost-effective substrates for a microelectronic vital parameter measurement system. Furthermore, we compare the measured technical parameters with existing copper-based counterparts on a FR4 substrate.
Smart Home or Smart Building applications are a growing market. An increasing challenge is to design energy efficient Smart Home applications to achieve sustainable and green homes. Using the example of the development of an Indoor Smart Gardening system with wireless monitoring and automated watering this paper is discussing in particular the design issue of energy autonomous working sensors and actuators for home automation. Most important part of the presented Smart Gardening system is a 3D printed smart flower pot for single plants. The smart flower pot has integrated a water reservoir for automated plant irrigation and an electronic for monitoring important plant parameters and the water level of the water reservoir. Energy harvesting with solar cells enables energy autonomous working of the flower pot. A low-power wireless interface also integrated in the flowerpot and an external gateway based on a Raspberry Pi 3 enables wireless networking of multiple of those flower pots. The gateway is used for evaluating the plant parameters and as a user interface. Particularly the architecture of the energy autonomous wireless flower pot will be considered, because fully energy autonomous sensors and actuators for home automation could not be implemented without special concepts for the energy supply and the overall electronic.
Printed electronics can benefit from the deployment of electrolytesas gate insulators,which enables a high gate capacitance per unit area (1–10 μFcm−2) due to the formation of electrical double layers (EDLs). Consequently, electrolyte-gated field-effect transistors (EGFETs) attain high-charge carrier densities already in the subvoltage regime, allowing for low-voltage operation of circuits and systems. This article presents a systematic study of lumped terminal capacitances of printed electrolyte-gated transistors under various dc bias conditions. We perform voltage-dependent impedancemeasurements and separate extrinsic components from the lumped terminal capacitance.
The proposed Meyer-like capacitance model, which also accounts for the nonquasi-static (NQS) effect, agrees well with experimental data. Finally, to verify the model, we implement it in Verilog-A and simulate the transient response of an inverter and a ring oscillator circuit. Simulation results are in good agreement with the measurement data of fabricated devices.
ABSCHIED VOM AUSSEN
(2019)
With this generation of devices, Virtual Reality (VR) has actually made it into the living rooms of end-users. These devices feature 6-DOF tracking, allowing them to move naturally in virtual worlds and experience them even more immersively. However, for a natural locomotion in the virtual, one needs a corresponding free space in the real environment. The available space is often limited, especially in everyday environments and under normal spatial conditions. Furnishings and objects of daily life can quickly become obstacles for VR users if they are not cleared away. Since the idea behind VR is to place users into a virtual world and to hide the real world as much as possible, invisible objects represent potential obstacles. The currently available systems offer only rudimentary assistance for this problem. If a user threatens to leave the space previously defined for use, a visual boundary is displayed to allow orientation within the space. These visual metaphors are intended to prevent users from leaving the safe area. However, there is no detection of potentially dangerous objects within this part of space. Objects that have not been cleared away or that have been added in the meantime may still become obstacles. This thesis shows how possible obstacles in the environment can be detected automatically with range imaging cameras and how users can be effectively warned about them in the virtual environment without significantly disturbing their sense of presence. Four different interactive visual metaphors are used to signalize the obstacles within the VE. With the help of a user study, the four signaling variants and the obstacle detection were evaluated and tested.
Dissertation D. Dongol
Process engineering industries are now facing growing economic pressure and societies' demands to improve their production technologies and equipment, making them more efficient and environmentally friendly. However unexpected additional technical and ecological drawbacks may appear as negative side effects of the new environmentally-friendly technologies. Thus, in their efforts to intensify upstream and downstream processes, industrial companies require a systematic aid to avoid compromising of ecological impact. The paper conceptualises a comprehensive approach for eco-innovation and eco- design in process engineering. The approach combines the advantages of Process Intensification as Knowledge-Based Engineering (KBE), inventive tools of Knowledge-Based Innovation (KBI), and main principles and best-practices of Eco-Design and Sustainable Manufacturing. It includes a correlation matrix for identification of eco-engineering contradictions and a process mapping technique for problem definition, database of Process Intensification methods and equipment, as well as a set of strongest inventive operators for eco-ideation.
As engineering graduates and specialists frequently lack the advanced skills and knowledge required to run eco-innovation systematically, the paper proposes a new teaching method and appropriate learning materials in the field of eco-innovation and evaluates the learning experience and outcomes. This programme is aimed at strengthening student’s skills and motivation to identify and creatively overcome secondary eco-contradictions in case if additional environmental problems appears as negative side effects of eco-friendly solutions.
Based on a literature analysis and own investigations, authors propose to introduce a manageable number of eco-innovation tools into a standard one-semester design course in process engineering with particular focus on the identification of eco-problems in existing technologies, selection of the appropriate new process intensification technologies (knowledge-based engineering), and systematic ideation and problem solving (knowledge-based innovation and invention).
The proposed educational approach equips students with the advanced knowledge, skills and competences in the field of eco-innovation. Analysis of the student’s work allows one to recommend simple-to-use tools for a fast application in process engineering, such as process mapping, database of eco-friendly process intensification technologies, and up to 20 strongest inventive operators for solving of environmental problems. For the majority of students in the survey, even the small workload has strengthened their self-confidence and skills in eco-innovation
Growing demands for cleaner production and higher eco-efficiency in process engineering require a comprehensive analysis of technical and environmental outcomes of customers and society. Moreover, unexpected additional technical or ecological drawbacks may appear as negative side effects of new environ-mentally friendly technologies. The paper conceptualizes a comprehensive ap-proach for analysis and ranking of engineering and ecological requirements in process engineering in order to anticipate secondary problems in eco-design and to avoid compromising the environmental or technological goals. For this purpose, the paper presents a method based on integration of the Quality Func-tion Deployment approach with the Importance-Satisfaction Analysis for the requirements ranking. The proposed method identifies and classifies compre-hensively the potential engineering and eco-engineering contradictions through analysis of correlations within requirements groups such as stakehold-er requirements (SRs) and technical requirements (TRs), and additionally through cross-relationship between SRs and TRs.
The 40 Altshuller Inventive Principles with numerous sub-principles remain over decades the most frequently applied tool of the Theory of Inventive Problem Solving TRIZ for systematic idea generation. However, their application often requires a concentrated, creative and abstract way of thinking that can be fairly challenging for the newcomers to TRIZ. This paper describes an approach to reduce the abstraction level of inventive sub-principles and presents the results of the idea generation experiment conducted with three groups of undergraduate and graduate students from different years of study in mechanical and process engineering. The students were asked to generate and to record their individual ideas for three design problems using a pre-defined set of classical and modified sub-principles within 10 minutes. The overall outcomes of the experiment support the assumption that the less abstract wording of the modified sub-principles leads to higher number of ideas. The distribution of ideas between the fields of MATCHEM-IBD (Mechanical, Acoustic, Thermal, Chemical, Electrical, Magnetic, Intermolecular, Biological and Data processing) differs significantly between groups using modified and abstract sub-principles.
Internationale Projektarbeit
(2019)
Die Einführung von Projektmanagementstandards kostet nachweislich Zeit und Geld, bringt vorübergehende Unruhe in die Organisation und ist nicht selten durch eine lästige Kundenforderung initiiert. Durch die Beschränkung der Sichtweise auf diese Aspekte wird das Thema häufig als unangenehm empfunden. Wir möchten die Implementierung von PM-Standards aber als lohnende Investition vorstellen, Potenziale, Chancen und Synergien aufzeigen und eine solide Basis für zahlreiche Organisations- und Verbesserungsprojekte zur Einführung von PM-Standards schaffen.
Die erste Auflage von "Ausführung von Stahlbauten" wurde – trotz ihres erheblichen Umfangs – zu einem sehr beliebten Nachschlagewerk und unverzichtbaren Praxishelfer in vielen Bereichen des Stahlbaus. Nachdem die Normen, auf die sich die erste Auflage bezieht, grundlegend überarbeitet wurden, entstand mit dem hier vorliegenden Buch auch eine aktualisierte Nachfolgeausgabe der Kommentare. Das Werk profitiert davon, dass es von den im Normungsausschuss federführenden Spezialisten ausgearbeitet wurde: Die Autoren liefern also zuverlässig die korrekten Interpretationen der Normentexte. Damit ist auch diese zweite Auflage von "Ausführung von Stahlbauten" eine gute Wahl, um mit den Normen DIN EN 1090-2 und DIN EN 1090-4 sicher und professionell arbeiten zu können. Da sich die Norm DIN EN 1090-1 aktuell in Überarbeitung befindet, ist diese nicht Teil der zweiten Auflage der Stahlbauten-Kommentare.
Im Beitrag werden nach einer kurzen Einführung zu hochfesten Stählen und dem Schweißen von hochfesten Stählen die gültigen und zukünftigen Bemessungsregeln von Schweißverbindungen mit hochfesten Stählen behandelt und an Beispielen erläutert. Es werden die für den Schweißprozess wichtigen Material‐ und Gefügeeingeschaften und Herstellungsverfahren von höherfesten Stählen beschrieben und die sich daraus einstellenden Anforderungen während des Schweißvorgangs dargelegt. Anhand von Beispielen werden die derzeit gültigen Bemessungsregeln für Schweißverbindungen mit höherfesten Stählen erläutert und ein neues durch Versuche abgesichertes Bemessungsmodell für Kehlnähte vorgestellt, das es erlaubt, gezielt den wichtigen Einfluss des Schweißzusatzwerkstoffs zu erfassen. Abschließend werden die aus numerischen Schweißsimulationen gewonnenen Erkentnisse hinsichtlich Temperatur, Gefüge, Eigenspannungszustand und auch Tragverhalten einer Schweißnaht beschrieben und durch Beispiele veranschaulicht.
Deep generative models have recently achieved impressive results for many real-world applications, successfully generating high-resolution and diverse samples from complex datasets. Due to this improvement, fake digital contents have proliferated growing concern and spreading distrust in image content, leading to an urgent need for automated ways to detect these AI-generated fake images.
Despite the fact that many face editing algorithms seem to produce realistic human faces, upon closer examination, they do exhibit artifacts in certain domains which are often hidden to the naked eye. In this work, we present a simple way to detect such fake face images - so-called DeepFakes. Our method is based on a classical frequency domain analysis followed by basic classifier. Compared to previous systems, which need to be fed with large amounts of labeled data, our approach showed very good results using only a few annotated training samples and even achieved good accuracies in fully unsupervised scenarios. For the evaluation on high resolution face images, we combined several public datasets of real and fake faces into a new benchmark: Faces-HQ. Given such high-resolution images, our approach reaches a perfect classification accuracy of 100% when it is trained on as little as 20 annotated samples. In a second experiment, in the evaluation of the medium-resolution images of the CelebA dataset, our method achieves 100% accuracy supervised and 96% in an unsupervised setting. Finally, evaluating a low-resolution video sequences of the FaceForensics++ dataset, our method achieves 91% accuracy detecting manipulated videos.
Recent studies have shown remarkable success in image-to-image translation for attribute transfer applications. However, most of existing approaches are based on deep learning and require an abundant amount of labeled data to produce good results, therefore limiting their applicability. In the same vein, recent advances in meta-learning have led to successful implementations with limited available data, allowing so-called few-shot learning.
In this paper, we address this limitation of supervised methods, by proposing a novel approach based on GANs. These are trained in a meta-training manner, which allows them to perform image-to-image translations using just a few labeled samples from a new target class. This work empirically demonstrates the potential of training a GAN for few shot image-to-image translation on hair color attribute synthesis tasks, opening the door to further research on generative transfer learning.
In this preliminary report, we present a simple but very effective technique to stabilize the training of CNN based GANs. Motivated by recently published methods using frequency decomposition of convolutions (e.g. Octave Convolutions), we propose a novel convolution scheme to stabilize the training and reduce the likelihood of a mode collapse. The basic idea of our approach is to split convolutional filters into additive high and low frequency parts, while shifting weight updates from low to high during the training. Intuitively, this method forces GANs to learn low frequency coarse image structures before descending into fine (high frequency) details. Our approach is orthogonal and complementary to existing stabilization methods and can simply plugged into any CNN based GAN architecture. First experiments on the CelebA dataset show the effectiveness of the proposed method.
Recent deep learning based approaches have shown remarkable success on object segmentation tasks. However, there is still room for further improvement. Inspired by generative adversarial networks, we present a generic end-to-end adversarial approach, which can be combined with a wide range of existing semantic segmentation networks to improve their segmentation performance. The key element of our method is to replace the commonly used binary adversarial loss with a high resolution pixel-wise loss. In addition, we train our generator employing stochastic weight averaging fashion, which further enhances the predicted output label maps leading to state-of-the-art results. We show, that this combination of pixel-wise adversarial training and weight averaging leads to significant and consistent gains in segmentation performance, compared to the baseline models.
Current training methods for deep neural networks boil down to very high dimensional and non-convex optimization problems which are usually solved by a wide range of stochastic gradient descent methods. While these approaches tend to work in practice, there are still many gaps in the theoretical understanding of key aspects like convergence and generalization guarantees, which are induced by the properties of the optimization surface (loss landscape). In order to gain deeper insights, a number of recent publications proposed methods to visualize and analyze the otimization surfaces. However, the computational cost of these methods are very high, making it hardly possible to use them on larger networks. In this paper, we present the GradVis Toolbox, an open source library for efficient and scalable visualization and analysis of deep neural network loss landscapes in Tesorflow and PyTorch. Introducing more efficient mathematical formulations and a novel parallelization scheme, GradVis allows to plot 2d and 3d projections of optimization surfaces and trajectories, as well as high resolution second order gradient information for large networks.
Most machine learning methods require careful selection of hyper-parameters in order to train a high performing model with good generalization abilities. Hence, several automatic selection algorithms have been introduced to overcome tedious manual (try and error) tuning of these parameters. Due to its very high sample efficiency, Bayesian Optimization over a Gaussian Processes modeling of the parameter space has become the method of choice. Unfortunately, this approach suffers from a cubic compute complexity due to underlying Cholesky factorization, which makes it very hard to be scaled beyond a small number of sampling steps. In this paper, we present a novel, highly accurate approximation of the underlying Gaussian Process. Reducing its computational complexity from cubic to quadratic allows an efficient strong scaling of Bayesian Optimization while outperforming the previous approach regarding optimization accuracy. First experiments show speedups of a factor of 162 in single node and further speed up by a factor of 5 in a parallel environment.
Thermisch angetriebene (Adsorptions-)Kältemaschinen können mit einem verhältnismäßig geringen elektrischen Energieaufwand bzw. mit einer hohen elektrischen Leistungszahl Kälte bereitstel-len. Wird die zum Antrieb erforderliche Wärme aus industrieller Abwärme bereitgestellt, ist diese Kältebereitstellung energetisch effizienter als die Kältebereitstellung über eine Kompressionskäl-temaschine. Wird die Wärme jedoch in Kraft-Wärme-Kopplung bereitgestellt, ist die primärenergetische Bewertung sowohl von mehreren Teilwirkungsgraden als auch den Primärenergiefaktoren für den eingesetzten Brennstoff und die erzeugte bzw. bezogene elektrische Energie abhängig. Eine umfangreiche Messkampagne im Sommer 2018 liefert unter realitätsnahen Randbedingungen in einer Labor umgebung detaillierte Energiekennzahlen für einen typischen Tagesgang des Kältebedarfs. Damit gelingt es, Teilenergiekennwerte für die Planungspraxis abzuleiten und das Gesamtsystem energetisch mit einer konventionellen Kompressionskältemaschine zu vergleichen.
One of the challenges for autonomous driving in general is to detect objects in the car's camera images. In the Audi Autonomous Driving Cup (AADC), among those objects are other cars, adult and child pedestrians andemergency vehicle lighting. We show that with recent deep learning networks weare able to detect these objects reliably on the limited Hardware of the model cars. Also, the same deep network is used to detect road features like mid lines, stoplines and even complete crossings. Best results are achieved using Faster R-CNNwith Inception v2 showing an overall accuracy of 0.84 at 7 Hz.
In this article we outline the model development planned within the joint projectModel-based city planningand application in climate change (MOSAIK). The MOSAIK project is funded by the German FederalMinistry of Education and Research (BMBF) within the frameworkUrban Climate Under Change ([UC]2)since 2016. The aim of MOSAIK is to develop a highly-efficient, modern, and high-resolution urban climatemodel that allows to be applied for building-resolving simulations of large cities such as Berlin (Germany).The new urban climate model will be based on the well-established large-eddy simulation code PALM, whichalready has numerous features related to this goal, such as an option for prescribing Cartesian obstacles. Inthis article we will outline those components that will be added or modified in the framework of MOSAIK.Moreover, we will discuss the everlasting issue of acquisition of suitable geographical information as inputdata and the underlying requirements from the model's perspective.