Modelling bacterial flagellar growth

The growth of bacterial flagellar filaments is a self-assembly process where flagellin molecules are transported through the narrow core of the flagellum and are added at the distal end. To model this situation, we generalize a growth process based on the TASEP model by allowing particles to move both forward and backward on the lattice. The bias in the forward and backward jump rates determines the lattice tip speed, which we analyze and also compare to simulations. For positive bias, the system is in a non-equilibrium steady state and exhibits boundary-induced phase transitions. The tip speed is constant. In the no-bias case we find that the length of the lattice grows as $N(t)\propto\sqrt{t}$, whereas for negative drift $N(t)\propto\ln{t}$. The latter result agrees with experimental data of bacterial flagellar growth.Comment: 6 pages, 7 figure

Bag-of-words representations for computer audition

Computer audition is omnipresent in everyday life, in applications ranging from personalised virtual agents to health care. From a technical point of view, the goal is to robustly classify the content of an audio signal in terms of a defined set of labels, such as, e.g., the acoustic scene, a medical diagnosis, or, in the case of speech, what is said or how it is said. Typical approaches employ machine learning (ML), which means that task-specific models are trained by means of examples. Despite recent successes in neural network-based end-to-end learning, taking the raw audio signal as input, models relying on hand-crafted acoustic features are still superior in some domains, especially for tasks where data is scarce. One major issue is nevertheless that a sequence of acoustic low-level descriptors (LLDs) cannot be fed directly into many ML algorithms as they require a static and fixed-length input. Moreover, also for dynamic classifiers, compressing the information of the LLDs over a temporal block by summarising them can be beneficial. However, the type of instance-level representation has a fundamental impact on the performance of the model. In this thesis, the so-called bag-of-audio-words (BoAW) representation is investigated as an alternative to the standard approach of statistical functionals. BoAW is an unsupervised method of representation learning, inspired from the bag-of-words method in natural language processing, forming a histogram of the terms present in a document. The toolkit openXBOW is introduced, enabling systematic learning and optimisation of these feature representations, unified across arbitrary modalities of numeric or symbolic descriptors. A number of experiments on BoAW are presented and discussed, focussing on a large number of potential applications and corresponding databases, ranging from emotion recognition in speech to medical diagnosis. The evaluations include a comparison of different acoustic LLD sets and configurations of the BoAW generation process. The key findings are that BoAW features are a meaningful alternative to statistical functionals, offering certain benefits, while being able to preserve the advantages of functionals, such as data-independence. Furthermore, it is shown that both representations are complementary and their fusion improves the performance of a machine listening system.Maschinelles Hören ist im täglichen Leben allgegenwärtig, mit Anwendungen, die von personalisierten virtuellen Agenten bis hin zum Gesundheitswesen reichen. Aus technischer Sicht besteht das Ziel darin, den Inhalt eines Audiosignals hinsichtlich einer Auswahl definierter Labels robust zu klassifizieren. Die Labels beschreiben bspw. die akustische Umgebung der Aufnahme, eine medizinische Diagnose oder - im Falle von Sprache - was gesagt wird oder wie es gesagt wird. Übliche Ansätze hierzu verwenden maschinelles Lernen, d.h., es werden anwendungsspezifische Modelle anhand von Beispieldaten trainiert. Trotz jüngster Erfolge beim Ende-zu-Ende-Lernen mittels neuronaler Netze, in welchen das unverarbeitete Audiosignal als Eingabe benutzt wird, sind Modelle, die auf definierten akustischen Merkmalen basieren, in manchen Bereichen weiterhin überlegen. Dies gilt im Besonderen für Einsatzzwecke, für die nur wenige Daten vorhanden sind. Allerdings besteht dabei das Problem, dass Zeitfolgen von akustischen Deskriptoren in viele Algorithmen des maschinellen Lernens nicht direkt eingespeist werden können, da diese eine statische Eingabe fester Länge benötigen. Außerdem kann es auch für dynamische (zeitabhängige) Klassifikatoren vorteilhaft sein, die Deskriptoren über ein gewisses Zeitintervall zusammenzufassen. Jedoch hat die Art der Merkmalsdarstellung einen grundlegenden Einfluss auf die Leistungsfähigkeit des Modells. In der vorliegenden Dissertation wird der sogenannte Bag-of-Audio-Words-Ansatz (BoAW) als Alternative zum Standardansatz der statistischen Funktionale untersucht. BoAW ist eine Methode des unüberwachten Lernens von Merkmalsdarstellungen, die von der Bag-of-Words-Methode in der Computerlinguistik inspiriert wurde, bei der ein Textdokument als Histogramm der vorkommenden Wörter beschrieben wird. Das Toolkit openXBOW wird vorgestellt, welches systematisches Training und Optimierung dieser Merkmalsdarstellungen - vereinheitlicht für beliebige Modalitäten mit numerischen oder symbolischen Deskriptoren - erlaubt. Es werden einige Experimente zum BoAW-Ansatz durchgeführt und diskutiert, die sich auf eine große Zahl möglicher Anwendungen und entsprechende Datensätze beziehen, von der Emotionserkennung in gesprochener Sprache bis zur medizinischen Diagnostik. Die Auswertungen beinhalten einen Vergleich verschiedener akustischer Deskriptoren und Konfigurationen der BoAW-Methode. Die wichtigsten Erkenntnisse sind, dass BoAW-Merkmalsvektoren eine geeignete Alternative zu statistischen Funktionalen darstellen, gewisse Vorzüge bieten und gleichzeitig wichtige Eigenschaften der Funktionale, wie bspw. die Datenunabhängigkeit, erhalten können. Zudem wird gezeigt, dass beide Darstellungen komplementär sind und eine Fusionierung die Leistungsfähigkeit eines Systems des maschinellen Hörens verbessert

Continuous emotion recognition in speech: do we need recurrence?

Emotion recognition in speech is a meaningful task in affective computing and human-computer interaction. As human emotion is a frequently changing state, it is usually represented as a densely sampled time series of emotional dimensions, typically arousal and valence. For this, recurrent neural network (RNN) architectures are employed by default when it comes to modelling the contours with deep learning approaches. However, the amount of temporal context required is questionable, and it has not yet been clarified whether the consideration of long-term dependencies is actually beneficial. In this contribution, we demonstrate that RNNs are not necessary to accomplish the task of time-continuous emotion recognition. Indeed, results gained indicate that deep neural networks incorporating less complex convolutional layers can provide more accurate models. We highlight the pros and cons of recurrent and non-recurrent approaches and evaluate our methods on the public SEWA database, which was used as a benchmark in the 2017 and 2018 editions of the Audio-Visual Emotion Challenge.ISSN: 1990-9772, Pages 2808-281

Aktive Dämpfung von Gleichtaktstörungen in elektrischen Antriebssystemen

Die vorliegende Arbeit beschäftigt sich mit der Entstehung und Wirkung von Gleichtaktstörungen in elektrischen Antriebssystemen. Parasitäre Gleichtaktimpedanzen bilden zusammen mit Gegentaktimpedanzen des Antriebssystems parasitäre Resonanzschwingkreise aus, welche von den Schalthandlungen der eingesetzten Umrichter im Antriebssystem zum Schwingen angeregt werden. Einer der beiden Schwerpunkte befasst sich mit der Entwicklung einer analytischen Berechnungsmethode, um die vorhandenen parasitären Gleichtaktpfade sowie deren Resonanzfrequenzen zu ermitteln. Der zweite Schwerpunkt behandelt die Möglichkeit der aktiven Dämpfung von Gleichtaktstörungen. Hierzu analysiert ein Algorithmus jede einzelne Schaltflanke im Umrichter auf deren Wirkung im Gleichtaktsystem. Anschließend kann diese zeitlich derart verschoben werden, dass die Schaltflanke eine bestmögliche Dämpfung im Gleichtaktsystem erzielt. Einleitend werden die notwendigen Grundlagen zur Umrichtertechnik sowie der Ausbildung und Anregung von Resonanzschwingkreisen am Beispiel eines RLC-Reihenschwingkreises erklärt, der stellvertretend für das stark reduzierte Gleichtaktmodell eines modernen Antriebssystems steht. Die Literaturrecherche gibt einen detaillierten Einblick über aktuelle Methoden der Reduktion von Gleichtaktstörungen wieder. Viele wissenschaftliche Veröffentlichungen behandeln modifizierte Ansteuerverfahren für die eingesetzten Umrichter, welche gezielt die Gleichtaktspannungen eines jeden Spannungsraumzeigers ausnutzen. Die Theorie und Funktionsweise des neuen Steuerverfahrens HCad, wird mathematisch hergeleitet und anhand eines idealen Simulationsmodells untersucht. Die Auswertung der Simulationsergebnisse ermöglicht es bereits, die dämpfende Wirkung unter idealen Bedingungen zu quantifizieren. Die Umsetzung eines praxisnahen Laborprüfstands dient der Verifikation und dem Vergleich realer Messungen mit den in der Simulation gewonnen Erkenntnissen. Die durchgeführten Messreihen bestätigen die Möglichkeit, Gleichtaktstörungen mithilfe einer zeitlichen Flankenverschiebung im Umrichter zu bekämpfen. Des Weiteren ist gezeigt, dass die Anwendung der aktiven Gleichtaktdämpfung neben der eigentlichen Aufgabe eines Steuerverfahrens in vollem Umfang und über den kompletten Modulationsbereich eines Umrichters möglich ist.This thesis investigates the formation and effect of common mode oscillation in electric drive systems. Parasitic impedances are forming resonant circuits in combination with the impedances of the drive system. Each inverter switching operation is stimulating common mode oscillation in system resonant circuits. One focus is the development of an analytical calculation method to determine the existing parasitic common mode paths and their resonance frequencies. The other main focus is the possibility of an active damping method of common mode oscillation. For this purpose, an algorithm analyzes each individual switching edge of the inverter for its effect in the common mode system. Subsequently, the timing of the switching edge is optimized so that it achieves the best possible damping in the common mode system. As an introduction, the necessary fundamentals of inverter technology and their control algorithm are shown. As well as the formation and excitation of resonant circuits are explained using the example of an RLC series resonant circuit. The literature research provides a detailed insight into current methods of reducing common mode oscillation. Many scientific publications deal with modified control methods, which specifically exploit the common mode voltages of each space vector. The introduced new control algorithm among to the category of modified control methods, is based on a direct current control method. The theory and operation of the mentioned control method is first mathematically described and examined using an ideal simulation model. The evaluation of these signals already serves to quantify the damping effect of shifting the switching edge. A test bench is used to verify the active damping method in a practical application of an electrical drive system. Therefore, the measurement results confirm the possibility of damping common mode oscillation by slightly shifting each switching edge in the inverter. In addition, it is shown that it is fully possible to control the demanded current alongside the control algorithm of the active damping method

Conceptual Foundations on Debiasing for Machine Learning-Based Software

Machine learning (ML)-based software’s deployment has raised serious concerns about its pervasive and harmful consequences for users, business, and society inflicted through bias. While approaches to address bias are increasingly recognized and developed, our understanding of debiasing remains nascent. Research has yet to provide a comprehensive coverage of this vast growing field, much of which is not embedded in theoretical understanding. Conceptualizing and structuring the nature, effect, and implementation of debiasing instruments could provide necessary guidance for practitioners investing in debiasing efforts. We develop a taxonomy that classifies debiasing instrument characteristics into seven key dimensions. We evaluate and refine our taxonomy through nine experts and apply our taxonomy to three actual debiasing instruments, drawing lessons for the design and choice of appropriate instruments. Bridging the gaps between our conceptual understanding of debiasing for ML-based software and its organizational implementation, we discuss contributions and future research

Energy and Economic Performance of Solar Cooling Systems World Wide

AbstractSolar thermal cooling systems have been installed as pilot projects in most regions of the world, but due to the low number of total installations there is not yet much experience available about system sizing and design. To counter the lack of experience and to evaluate the potential of energy efficient solar cooling systems, a systematic system design study has been carried out covering most climatic regions worldwide. For each technology investigated, an energy optimized control strategy was developed which maximizes the primary energy efficiency. This control strategy was implemented in the simulation environment INSEL and system models were developed for a range of thermal cooling technologies and validated with operating experiences from different plants monitored by the authors.It could be shown that a reduction of nominal chiller power by 30% to 40% or more hardly effects the solar cooling fraction for most climates, but significantly increases the machine operating hours and thus improves the economics. The lower the nominal power of the chiller, the higher the recommended ratio of collector surface area per kW. For a given machine nominal power, solar cooling fractions increase with collector surface area until saturation is reached. Collector surface areas can be as high as 5 m2 to 10 m2 per kW with still increasing solar cooling fractions, but acceptable specific collector yield reduction. The economic optimum is reached for less solar cooling fraction and thus lower primary energy savings. Single effect absorption cooling systems easily reach 80% solar cooling fraction for all but very humid climates. Primary energy ratios can be over 3.0, depending on system design and cooling load data. CO2 and primary energy savings of 30 – 79% are achievable.The economic study showed that solar thermal cooling is more viable in hot climates than in moderate European climates. Annual costs strongly depend on the locations. The specific costs per kWh cooling in German locations vary between 0.25 and 1.01 €/kWh, in Spanish locations between 0.13 and 0.30 €/kWh. In hot climates like Jakarta and Riyadh the specific costs are as low as 0.09 to 0.15 €/kWh. Furthermore the maximum investment costs were calculated get a payback time of 10 years

Experimental Investigation on the Use of a PEI Foam as Core Material for the In-Situ Production of Thermoplastic Sandwich Structures Using Laser-Based Thermoplastic Automated Fiber Placement

Laser-based thermoplastic automated fiber placement (TAFP) is nowadays mainly used to produce pure carbon fiber-reinforced plastic (CFRP) structures. This paper investigates the feasibility of a novel application: The deposition of thermoplastic prepreg tapes onto a thermoplastic foam for the production of thermoplastic sandwich structures. Therefore, simple deposition experiments of thermoplastic PEEK/CF prepreg tapes on a PEI closed-cell foam were carried out. 3D surface profile measurements and peel tests according to DIN EN 28510-1 standard were used to investigate the joining area and bonding quality. The results show that a cohesive bond is formed between the deposited tapes and the foam core, however the foam structure in the area of the deposited tapes deforms in dependence of the process parameters, and increasingly with higher deposition temperatures. Due to the deformations that occur during tape deposition, the thermomechanical foam behavior under the TAFP process conditions was investigated in more detail in a subsequent study for an extensive parameter space using a simple experimental setup. Results show that for suitable process parameters, namely a short contact time and a high temperature, the foam deformation can be minimized with the simultaneous formation of a thin melting layer required for cohesive bonding. The inner foam core structure remains unaffected