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Weather radars are highly sophisticated tools for quantitative precipitation estimation (QPE) and provide observations with unmatched spatial representativeness. However, their indirect measurement of precipitation high above ground leads to strong systematic errors compared to direct rain gauge measurements. Additionally, the temporal undersampling from 5-min instantaneous radar measurements requires advection correction. We present ResRadNet, a 3-D-convolutional residual neural network approach, to reduce these errors and, at the same time, increase the temporal resolution of the radar rainfall fields by a 5-min short-range prediction of 1-min time-steps. The network is trained to process spatiotemporal sequences of radar rainfall estimates from a composite product derived from 17 C-band weather radars in Germany. In contrast to previous approaches, we present a method that emphasizes the generation of spatiotemporally consistent and advection-corrected country-wide rainfall maps. Our approach significantly increased the Pearson correlation coefficient (PCC) of the radar product (from 0.63 to 0.74) and decreased the root mean squared error (mse) by 22% when compared to 247 rain gauges at a 5-min resolution. An additional large-scale comparison to eight years of data from 1138 independent manual daily gauges confirmed that the improvement is robust and transferable to new locations. Overall, our study shows the benefits of using 3-D convolutional neural networks (CNNs) for weather radar rainfall estimation to provide 1-min, ground-adjusted, that is, bias-corrected with respect to on-ground sensors, and advection-corrected radar rainfall estimates.
Assessing Heuristic Evaluation in Immersive Virtual Reality: A Case Study on Future Guidance Systems
(2023)
A variety of evaluation methods for user interfaces (UI) exist such as usability testing, cognitive walkthrough, and heuristic evaluation. However, UIs such as guidance systems at transit hubs must be evaluated in their intended application field to allow the effective and valid identification of usability flaws. However, what if evaluations are not feasible in real environments, or laboratorial conditions cannot be ensured? Based on adapted heuristics, in the present study, the method of heuristic evaluation is combined with immersive Virtual Reality (VR) for the identification of usability flaws of dynamic guidance systems (DGS) at transit hubs. The study involved usability evaluations of nine DGS concepts using the newly proposed method. The results show that compared to computer-based heuristic evaluations, the use of immersive VR led to the identification of an increased amount of “severe” usability flaws as well as overall usability flaws. Within a qualitative assessment, immersive VR is validated as a suitable tool for conducting heuristic evaluations involving significant advantages such as the creation of realistic experiences in laboratorial conditions. Future work seeks to further prove the suitability of using immersive VR for heuristic evaluations and compare the proposed method to other evaluative methods.
Abstract
Motivation
Recognition of specific molecules by proteins is a fundamental cellular mechanism and relevant for many applications. Being able to modify binding is a key interest and can be achieved by repurposing established interaction motifs. We were specifically interested in a methodology for the design of peptide binding modules. By leveraging interaction data from known protein structures, we plan to accelerate the design of novel protein or peptide binders.
Results
We developed ATLIGATOR—a computational method to support the analysis and design of a protein’s interaction with a single side chain. Our program enables the building of interaction atlases based on structures from the PDB. From these atlases pocket definitions are extracted that can be searched for frequent interactions. These searches can reveal similarities in unrelated proteins as we show here for one example. Such frequent interactions can then be grafted onto a new protein scaffold as a starting point of the design process. The ATLIGATOR tool is made accessible through a python API as well as a CLI with python scripts.
Availability and implementation
Source code can be downloaded at github (https://www.github.com/Hoecker-Lab/atligator), installed from PyPI (‘atligator’) and is implemented in Python 3.
An electric traction machine for an electric or a hybrid electric vehicle is usually designed for a specific operating point or cycle. For such an operating point or cycle, the masses and the cooling circuit of the electric machine determine the time dependent temperature distribution within the machine. For a specific load cycle, the thermal simulation of the machine can reveal possible mass and size reductions for a given insulation class of the machine. In addition, such simulations allow the comparison of various cooling concepts. In the machine design process, the first step is a conventional electromagnetic machine design. From the geometric data of this design and the material properties, the parameters of a thermal equivalent circuit can be derived. The differential and algebraic equations of the thermal equivalent circuit are solved by a simulation tool to predict the temperatures of the critical parts in the electric machine. A thermal equivalent circuit is accurate enough to predict the thermal behavior of the critical parts in the electric machine, and yet not too complex, to obtain simulation results with moderate numerical effort. This enables an iterative design process to optimize the drive.
Radar data may potentially provide valuable information for precipitation quantification, especially in regions with a sparse network of in situ observations or in regions with complex topography. Therefore, our aim is to conduct a feasibility study to quantify precipitation intensities based on radar measurements and additional meteorological variables. Beyond the well-established Z–R relationship for the quantification, this study employs Artificial Neural Networks (ANNs) in different settings and analyses their performance. For this purpose, the radar data of a station in Upper Bavaria (Germany) is used and analysed for its performance in quantifying in situ observations. More specifically, the effects of time resolution, time offsets in the input data, and meteorological factors on the performance of the ANNs are investigated. It is found that ANNs that use actual reflectivity as only input are outperforming the standard Z–R relationship in reproducing ground precipitation.
This is reflected by an increase in correlation between modelled and observed data from 0.67 (Z–R) to 0.78 (ANN) for hourly and 0.61 to 0.86, respectively, for 10 min time resolution. However, the focus of this study was to investigate if model accuracy benefits from additional input features. It is shown that an expansion of the input feature space by using time-lagged reflectivity with lags up to two and additional meteorological variables such as temperature, relative humidity, and sunshine duration significantly increases model performance. Thus, overall, it is shown that a systematic predictor screening and the correspondent extension of the input feature space substantially improves the performance of a simple Neural Network model. For instance, air temperature and relative humidity provide valuable additional input information. It is concluded that model performance is dependent on all three ingredients: time resolution, time lagged information, and additional meteorological input features. Taking all of these into account, the model performance can be optimized to a correlation of 0.9 and minimum model bias of 0.002 between observed and modelled precipitation data even with a simple ANN architecture.
In an industrial product development process, the Bill of Materials (BOM) is a hierarchical, multi-level representation of all components, parts and quantities of a product. With increasing complexity of industrial products, also BOMs become more complex and thus prone to errors, for example when the individual parts of a product are changed during the product development process. Frequently, these Bill of Materials errors have to be identified manually or by using simple, rule-based schemes. In this paper, we provide a technical background of BOMs, showing the intricacy of temporal BOMs errors in an industrial product development process. The work of other authors, which focused on association mining and tree reconciliation to detect Bill of Materials errors, is analysed. We found that there is currently no system being able to prescribe where in a Bill of Materials and when in the product development process, errors are probable to occur. Also, Machine Learning (ML) methods have not been applied yet. Based on these findings, we formalize the notions Bill of Materials and Bill of Materials errors. Furthermore, we present a deterministic distance measure for BOMS. We provide an answer to the main question of how to represent a Bill of Materials for Machine Learning tasks by solving the orthogonal Procrustes problem for dynamic, hierarchical datasets. Then, we describe an isolation forest based approach to temporal anomaly detection, which points at potential errors in a Bill of Materials at a specific timestamp. Furthermore, we apply Machine Learning and present a multi-output Multi Layer Perceptron for the prediction of temporal Bill of Materials errors. The model predicts where and at which point of time Bill of Materials errors are probable to occur, which renders it a prescriptive system. Eventually, we optimize the performance of our model using contextualization via -means clustering. Finally, we apply our prescriptive pipeline to a real world dataset and show its superiority to existing methods using a qualitative comparison.
Thermal cyclers are used to perform polymerase chain reaction runs (PCR runs) and Peltier modules are the key components in these instruments. The demand for thermal cyclers has strongly increased during the COVID-19 pandemic due to the fact that they are important tools used in the research, identification, and diagnosis of the virus. Even though Peltier modules are quite durable, their failure poses a serious threat to the integrity of the instrument, which can lead to plant shutdowns and sample loss. Therefore, it is highly desirable to be able to predict the state of health of Peltier modules and thus reduce downtime. In this paper methods from three sub-categories of supervised machine learning, namely classical methods, ensemble methods and convolutional neural networks, were compared with respect to their ability to detect the state of health of Peltier modules integrated in thermal cyclers. Device-specific data from on-deck thermal cyclers (ODTC®) supplied by INHECO Industrial Heating & Cooling GmbH (Fig 1), Martinsried, Germany were used as a database for training the models. The purpose of this study was to investigate methods for data-driven condition monitoring with the aim of integrating predictive analytics into future product platforms. The results show that information about the state of health can be extracted from operational data - most importantly current readings - and that convolutional neural networks were the best at producing a generalized model for fault classification.
Prior research studies on supplier selection factors assess vendor reputation as a low ranked criterion. Reputation in these articles, however, only refers to the position in the industry, without considering the role of the final customer. Our results from a survey with 565 individuals suggest that the end user as a stakeholder should be also considered when analyzing a vendor’s prestige. We demonstrate that a supplier’s standing has negative as well as positive reputational effects on the buyer. Depending on the relevance of the purchased good as well as the reputation of the supplier and the buyer, low/high-ranked ingredients significantly decrease/increase the final customer’s perception of the buyer.
In a three-guide coupler with multimode central waveguide, more than two modes of the entire structure participate in the coupling between the outer waveguides. Using a three-mode approximation we found simple conditions for complete power transfer between the outer waveguides: the device length has to match certain multiples of the con- ventionally defined coupling length. The specific form of the relevant modes allows us to design a magneto-optic isolator or circulator with significantly reduced device length (as compared to the conventional nonreciprocal coupler). The performance of the proposed devices is simulated by propagating-mode calculations. Estimates for admissible fabrication tolerances for the layer thicknesses are presented.
Each particular implementation of the beam propagation method (BPM) requires a special procedure allowing for radiation to leave the computational window. We propose a new approach to constructing the finite difference schemes of the BPM at the boundary of the computational window. These schemes are independent of the computed fields and allow for a similar treatment of both interior and boundary points. The new approach can be further improved by correcting the field values at the boundary points according to Hadley's method. The algorithm is easy to implement for both two- and three-dimensional structures. The new method considerably reduces computation times because the propagation matrices remain constant in longitudinally invariant sections, thus avoiding repeated LU-decompositions. The basic idea-establishing the finite difference scheme such that locally exact, approximate, or plausible solutions are recovered-may be of interest for other efforts to solve partial differential equations by the finite difference method.
Effective coupling between two remote optical waveguides without branching sections can be achieved in a three-guide system with multimode central waveguide. We investigate the nonlinear power switching of c.w. laser radiation by such radiatively coupled waveguides. It is shown that effective all-optical switches with spatially well separated input/output channels can be realized although the influence of multimode interference on the switching characteristics becomes more pronounced for increasing thickness of the central guide. Different coupling regimes are specified, and the changes in switching characteristics during the transformation from one regime to another is studied. Numerical calculations for the critical power are compared with an approximate analytical expression. It is also shown that, at a moderate input power, a small number of modes determines the switching behavior.
Right-wing populism challenges the societies in Europe. The rise of such movements and parties are a threat to peace and democracy in Germany. This article focuses the possibilities as well as the boundaries of political education under the current circumstances. To this end, it discusses theoretical explanations based on approaches in peace education, political didactics and social sciences to deal with right-wing populism in practice. (DIPF/Orig.).
The performance of nanoelectronic and molecular electronic devices relies strongly on the employed functional units and their addressability, which is often a matter of appropriate interfaces and device design. Here, we compare two promising designs to build solid-state electronic devices utilizing the same functional unit. Optically addressable Ru-terpyridine complexes were incorporated in supramolecular wires or employed as ligands of gold nanoparticles and contacted by nanoelectrodes. The resulting small-area nanodevices were thoroughly electrically characterized as a function of temperature and light exposure. Differences in the resulting device conductance could be attributed to the device design and the respective transport mechanism, that is, thermally activated hopping conduction in the case of Ru-terpyridine wire devices or sequential tunneling in nanoparticle-based devices. Furthermore, the conductance switching of nanoparticle-based devices upon 530 nm irradiation was attributed to plasmon-induced metal-to-ligand charge transfer in the Ru-terpyridine complexes used as switching ligands. Finally, our results reveal a superior device performance of nanoparticle-based devices compared to molecular wire devices based on Ru-terpyridine complexes as functional units.
Purpose
The purpose of this paper is to explore the impact of information technology (IT) on supply chain performance in the automotive industry. Prior studies that analyzed the impact of IT on supply chain performance report results representing the situation of the “average industry.” This research focuses on the automotive industry because of its major importance in many national economies and due to the fact that automotive supply chains do not represent the supply chain of the average industry.
Design/methodology/approach
A research model is proposed to examine the relationships between IT capabilities, supply chain capabilities, and supplier performance. The model divides IT capabilities into functional and data capabilities, and supply chain capabilities into internal process excellence and information sharing. Data have been collected from 343 automotive first-tier suppliers. Structural equation modeling with partial least squares is used to analyze the data.
Findings
The results suggest that functional capabilities have the greatest impact on internal process excellence, which in turn enhances supplier performance. However, frequent and adequate information sharing also contributes significantly to supplier performance. Data capabilities enable supply chain capabilities through their positive impact on functional capabilities.
Practical implications
The findings will help managers to understand the effect of IT implementation on company performance and to decide whether to invest in the expansion of IT capacities.
Originality/value
This research reports the impact of IT on supply chain performance in one of the most important industries in many industrialized countries, and it provides a new perspective on evaluating the contribution of IT on firm performance.
A simple method for fabricating gain coupled DFB lasers with lateral surface chromium Bragg gratings and self-aligned low capacitance ohmic contacts is reported. These InGaAsP/InP lasers show monomode emission with a sidemode suppression ratio of 40 dB. For 243 µm long devices a continuous wave threshold current of 17 mA was measured at room temperature.
Incremental spoken dialogue systems, which process user input as it unfolds, pose additionalengineering challenges compared to more standard non-incremental systems: Their processingcomponents must be able to accept partial, and possibly subsequently revised input, and mustproduce output that is at the same time as accurate as possible and delivered with as little delay aspossible. In this article, we define metrics that measure how well a given processor meets thesechallenges, and we identify types of gold standards for evaluation. We exemplify these metrics inthe evaluation of several incremental processors that we have developed. We also present genericmeans to optimise some of the measures, if certain trade-offs are accepted. We believe that thiswork will help enable principled comparison of components for incremental dialogue systems andportability of results.
It is well known that mechanical surface treatments, such as deep rolling, shot peening and laser shock peening, can significantly improve the fatigue behavior of highly-stressed metallic components. Deep rolling (DR) is particularly attractive since it is possible to generate, near the surface, deep compressive residual stresses and work hardened layers while retaining a relatively smooth surface finish. In the present investigation, the effect of DR on the low-cycle fatigue (LCF) and high-cycle fatigue (HCF) behavior of a Ti–6Al–4V alloy is examined, with particular emphasis on the thermal and mechanical stability of the residual stress states and the near-surface microstructures. Preliminary results on laser shock peened Ti–6Al–4V are also presented for comparison. Particular emphasis is devoted to the question of whether such surface treatments are effective for improving the fatigue properties at elevated temperatures up to ∼450 °C, i.e. at a homologous temperature of ∼0.4T/Tm (where Tm is the melting temperature). Based on cyclic deformation and stress/life (S/N) fatigue behavior, together with the X-ray diffraction and in situ transmission electron microscopy (TEM) observations of the microstructure, it was found that deep rolling can be quite effective in retarding the initiation and initial propagation of fatigue cracks in Ti–6Al–4V at such higher temperatures, despite the almost complete relaxation of the near-surface residual stresses. In the absence of such stresses, it is shown that the near-surface microstructures, which in Ti–6Al–4V consist of a layer of work hardened nanoscale grains, play a critical role in the enhancement of fatigue life by mechanical surface treatment.
Studies focusing on the supply side of IP management, particularly on the outsourcing of patent related work, are relatively rare. This paper aims to contribute to the IP literature in two ways. First, a definition of the IP service provider is proposed. Second, I consider three main hypotheses that determine a firm's preference for outsourced IP work. Using data on the outsourcing of patent renewal payments, I found evidence that the choice of IP supplier is affected by: (1) the firm's own IP maintenance capabilities, (2) the firm's IP knowledge utilization, and (3) the IP complexity.
PurposeIn the intellectual property (IP) and management literature, the question of how external patent attorneys impact patent filings has been understudied. The purpose of this paper is to advance this area of research by examining how the use of external patent attorneys influences the patent filing strategies of firms and what impact firms? level of experience with the exclusive use of in-house resources has on filing strategies. This study, thus, provides insights into the strategic dimension behind patent filing, a process which is affected by patent attorneys? work and decision-making processes.Design/methodology/approachThe econometric analysis is based on a patent database of 922,553 patents which is combined with an EPO patent database covering applications from 1990 to 2010. The authors test the hypotheses for this study using patent indicators addressing the impact of in-house firm experience vs the use of external patent attorneys on firm?s filing strategy.FindingsThis research finds empirical evidence that external patent attorneys? work has an effect on patent scope, international scope, and patenting speed. Moreover, it can be shown that external patent attorneys have a positive impact on most filing dimensions, such as patent scope, international scope and the Patent Cooperation Treaty option, whereas the level of in-house firm experience has a negative impact on most filing dimensions. This implies that external patent attorneys seem to pursue a ?maximization approach? while experienced firms seem to pursue a more differentiated approach to filing patents, for instance, drafting narrower and more focused patents.Practical implicationsThe study suggests that effective filing strategies require an integrated approach between diverse IP stakeholders. More particularly, filing strategies should be communicated and aligned between all actors, including external patent attorneys in order to achieve the targeted patenting output.Originality/valueThe current study develops a patent filing typology, which accounts for patent attorneys? decision options. In providing insights into patent attorneys? work and their impacts on intellectual property rights management, the study is a useful complement to prior research, which has predominantly focused on applicants or examiners.
We report on dynamic measurements on antiphase complex coupled 1.55 /spl mu/m DFB lasers. The antiphase complex coupling mechanism is realized by periodically etching the active layer and quarternary InGaAsP overgrowth. This new fabrication approach results in very narrow linewidths measured on devices with rather short resonator lengths. The minimum linewidth for a 375 /spl mu/m long ridge waveguide laser was only 250 kHz at an optical output power of 4 mW. The RC-limited 3dB-bandwidth for those devices was /spl sim/6 GHz. Using circuit model corrections, the intrinsic bandwidth limitation of those lasers was found to be 17.4 GHz.
We present a toolkit for manipulating andvisualising time-aligned linguistic datasuch as dialogue transcripts or languageprocessing data. The package comple-ments existing editing tools by allowingfor conversion between their formats, in-formation extraction from the raw files,and by adding sophisticated, and easily ex-tended methods for visualising the dynam-ics of dialogue processing. To illustratethe versatility of the package, we describeits use in three different projects at our site.
Comparing Local and Sequential Models for Statistical Incremental Natural Language Understanding
(2010)
Incremental natural language understanding is the task of assigning semantic representations to successively larger prefixes of utterances. We compare two types of statistical models for this task: a) local models, which predict a single class for an input; and b), sequential models, which align a sequence of classes to a sequence of input tokens. We show that, with some modifications, the first type of model can be improved and made to approximate the output of the second, even though the latter is more informative. We show on two different data sets that both types of model achieve comparable performance (significantly better than a baseline), with the first type requiring simpler training data. Results for the first type of model have been reported in the literature; we show that for our kind of data our more sophisticated variant of the model performs better.
Participants in a conversation are normally receptive to their surroundings and their interlocutors, even while they are speaking and can, if necessary, adapt their ongoing utterance. Typical dialogue systems are not receptive and cannot adapt while uttering. We present combin-able components for incremental natural lan-guage generation and incremental speech syn-thesis and demonstrate the flexibility they can achieve with an example system that adapts to a listener's acoustic understanding problems by pausing, repeating and possibly rephrasing problematic parts of an utterance. In an evaluation, this system was rated as significantly more natural than two systems representing the current state of the art that either ignore the interrupting event or just pause; it also has a lower response time.
When dialogue systems, through theuse of incremental processing, arenot bounded anymore by strict, non-overlapping turn-taking, a whole range ofadditional interactional devices becomesavailable. We explore the use of one suchdevice, trial intonation. We elaborateour approach to dialogue managementin incremental systems, based on theInformation-State-Update approach, anddiscuss an implementation in a micro-domain that lends itself to the use ofimmediate feedback, trial intonations andexpansions. In an overhearer evaluation,the incremental system was judged as sig-nificantly more human-like and reactivethan a non-incremental version.
If we can model the cognitive and communicative processes underlying speech, we should be able to better predict what a speaker will do. With this idea as inspiration, we examine a number of prosodic and timing features as potential sources of information on what words the speaker is likely to say next. In spontaneous dialog we find that word probabilities do vary with such features. Using perplexity as the metric, the most informative of these included recent speaking rate, volume, and pitch, and time until end of utterance. Using simple combinations of such features to augment trigram language models gave up to a 8.4% perplexity benefit on the Switchboard corpus, and up to a 1.0% relative reduction in word error rate (0.3% absolute) on the Verbmobil II corpus.
Self-assembled and coherently strained Ge dots were grown on a Si/SiGe superlattice, which was deposited on a flat Si(001) substrate surface patterned with a regular array of straight trenches. The superlattice translates the surface modulation of the substrate into a strain-field modulation, which causes the Ge dots on its surface to form along straight lines above the buried trenches. This approach provides self-assembled Ge dots with excellent lateral periodicity, which might be useful for fabricating dot-based field-effect-transistors (DotFETs). Here, we propose the concept of a modulation-doped p-channel DotFET (p-MOD-DotFET). The p-MOD-DotFET relies on embedded Ge-rich nanostructures, which provide p-channels through the Ge-rich dots. A high Ge concentration in the dots is desirable in order to exploit the high hole mobility of Ge-rich material. We show that the commonly observed Si–Ge intermixing during Si capping of Ge dots can be suppressed by overgrowing the islands at low temperature.
Various theoretical models (self-consistent field, local linearization, and percolation theory methods and an analytic solution of the linear problem for an ordered medium) for calculating the magnetostatic properties of two-phase composites containing one ferromagnetic phase were considered. The concentration and field dependences of the effective magnetic permeability were found. A method for determining the coercive force and remanent magnetization as functions of the ferromagnetic phase concentration was suggested. Numerical experiments were performed for composites with a periodic distribution of circular inclusions. The results were compared with the analytically calculated effective magnetic permeability.
Two analytical methods based on the so-called local linearization of magnetostatic properties of composite materials with ferromagnetic inclusions in a non-magnetic matrix are described. These methods are applied to the experimental data obtained in Gorkunov et al. [Russ. J. Nondestruct. Test. 3 (2001) 186]. A qualitative agreement is obtained for the concentration values reported in that paper and a quantitative agreement is achieved at slightly different value of concentration. The conclusions are supported by direct numerical modeling.
Illumination of ac-biased photorefractive Bi12TiO20 crystals with a coherent light beam results in the development of strong nonlinear scattering. Theoretically and experimentally we investigate the angular and polarization characteristics of the scattered light for the diagonal ([1̲1̲1]) optical configuration and different polarization states of the pump. A satisfactory understanding of the observed scattering properties is achieved for most of the cases investigated.
Rare-earth iron garnet films with in-plane anisotropy grown on (111)-oriented substrates can be used as magneto-optical indicator films for visualization of magnetic leakage fields in nondestructive evaluation. The influence of Faraday rotation, Faraday ellipticity, absorption and film thickness on the performance of a magneto-optical indicator film is investigated. A new optimization method is introduced and compared with the method of contrast optimization. The theory is experimentally verified and an application example is presented.
We investigate the feasibility of using rare-earth iron garnet films grown on (111)-oriented substrates as magneto-optic indicator films for the visualization of magnetic leakage fields in non-destructive evaluation. In most cases the leakage field of the defect has a strong component in the film plane. The influence of this in-plane dc magnetic field on the image formation is investigated. It is shown that the presence of a strong in-plane magnetic field allows one to extend the dynamic range of the out-of-plane field imposed by the uniaxial anisotropy field HA. On the other hand, an in-plane field reduces the sensitivity. The guidelines for selecting parameters of magneto-optic indicator films are given.
217 Teilnehmer/innen wurden mittels standardisierter Fragebögen zu ihrer Informiertheit über das Hilfesystem bei Demenz sowie zu ihren eigenen Versorgungswünschen im Falle einer Demenzerkrankung befragt. Bei den Probanden handelt es sich um Personen mit und ohne pflegebedürftige Angehörige. Jene wurden hinsichtlich ihrer Versorgungssituation in drei Gruppen unterteilt: ambulante Pflege, Tagespflege und Altenpflegeheime. Die Ergebnisse zeigen, dass in allen Untersuchungsgruppen ein im Allgemeinen geringer Informationsstand in Bezug auf die unterschiedlichen Hilfs- und Entlastungsangebote für Angehörige dementer Patienten vorherrscht. Zu den bevorzugten Versorgungsformen im Falle eigener Betroffenheit gehören ambulante sowie neue, alternative Pflegemodelle.
Durch die voranschreitende Digitalisierung bieten sich neue Ansätze für Designs und Methoden in der Jugendforschung. Mit vielfältigen digitalen Möglichkeiten wachsen auch Chancen für inklusiv angelegte Befragungen. Dieser Beitrag fokussiert exemplarisch gehörlose bzw. hörbeeinträchtigte gebärdensprachige Jugendliche und stellt nach einem theoretischen und empirischen Überblick die Frage, ob und inwieweit diese heterogene Gruppe Jugendlicher durch in digitale Fragebögen implementierte Videos in Gebärdensprache profitieren kann. Anhand zweier kontrastiver Fallvignetten sollen Erfahrungen aus dem Projekt „Inklusive Methoden“ in Bezug auf Möglichkeiten, Voraussetzungen, Herausforderungen und Grenzen digitaler Befragungen mit Gebärdensprachvideos dargestellt und diskutiert werden.
Garnet films of high Faraday rotation are applied as magneto-optical sensors to visualize the spatial distribution of magnetic fields. Using specific orientations, the induced anisotropy can generate an easy plane of magnetization which is inclined with respect to the film plane. If the magnetization lies in this plane a very high sensitivity can be achieved. The dependence of the geometrical orientation of the easy plane on the growth direction is calculated and the sensitivity and dynamic range are derived. Experimental results of a [112] oriented garnet film are in good agreement with calculations.
This paper describes a case study of assessing student's coding behavior and skills in a realistic development setting. Students had to solve typical programming problems in the context of app development for the Android platform using the Eclipse IDE. Data was analyzed using IDE as well as browser interaction logs. In addition, screen recordings of the students' interaction with the IDE provide further insight. In this paper we present the first results of our ongoing work.
EMDLAB: A toolbox for analysis of single-trial EEG dynamics using empirical mode decomposition
(2015)
Background:
Empirical mode decomposition (EMD) is an empirical data decomposition technique. Recently there is growing interest in applying EMD in the biomedical field.
New method:
EMDLAB is an extensible plug-in for the EEGLAB toolbox, which is an open software environment for electrophysiological data analysis.
Results:
EMDLAB can be used to perform, easily and effectively, four common types of EMD: plain EMD, ensemble EMD (EEMD), weighted sliding EMD (wSEMD) and multivariate EMD (MEMD) on EEG data. In addition, EMDLAB is a user-friendly toolbox and closely implemented in the EEGLAB toolbox.
Comparison with existing methods:
EMDLAB gains an advantage over other open-source toolboxes by exploiting the advantageous visualization capabilities of EEGLAB for extracted intrinsic mode functions (IMFs) and Event-Related Modes (ERMs) of the signal.
Conclusions:
EMDLAB is a reliable, efficient, and automated solution for extracting and visualizing the extracted IMFs and ERMs by EMD algorithms in EEG study.
This paper proposes a multi domain physical model of permanent magnet synchronous machines, considering electrical, magnetic, thermal and mechanical effects. For each component of the model, the main wave as well as lower and higher harmonic wave components of the magnetic flux and the magnetic potential difference may be considered. The permanent magnets are modeled as discrete azimuthal segments in order to consider possible partial demagnetization effects. For each magnet segment a linearized temperature dependent B-H curve is considered. The main advantage of the presented model is that time transient operational behavior of a permanent magnet synchronous machine can be considered under various electrical, magnetic, thermal and mechanical conditions. The electromagnetic condition of surface magnet machine is compared with finite element analysis.
Patients with age-related macular degeneration (AMD) or hereditary macular dystrophies (JMD) rely on an efficient use of their peripheral visual field. We trained eight AMD and five JMD patients to perform a texture-discrimination task (TDT) at their preferred retinal locus (PRL) used for fixation. Six training sessions of approximately one hour duration were conducted over a period of approximately 3 weeks. Before, during and after training twelve patients and twelve age-matched controls (the data from two controls had to be discarded later) took part in three functional magnetic resonance imaging (fMRI) sessions to assess training-related changes in the BOLD response in early visual cortex. Patients benefited from the training measurements as indexed by significant decrease (p = 0.001) in the stimulus onset asynchrony (SOA) between the presentation of the texture target on background and the visual mask, and in a significant location specific effect of the PRL with respect to hit rate (p = 0.014). The following trends were observed: (i) improvement in Vernier acuity for an eccentric line-bisection task; (ii) positive correlation between the development of BOLD signals in early visual cortex and initial fixation stability (r = 0.531); (iii) positive correlation between the increase in task performance and initial fixation stability (r = 0.730). The first two trends were non-significant, whereas the third trend was significant at p = 0.014, Bonferroni corrected. Consequently, our exploratory study suggests that training on the TDT can enhance eccentric vision in patients with central vision loss. This enhancement is accompanied by a modest alteration in the BOLD response in early visual cortex.
Hereditary and age-related forms of macular dystrophy (MD) are characterized by loss of cone function in the fovea, leading to central scotomata and eccentric fixation at the so-called preferred retinal locus (PRL). We investigated whether perceptual learning enhances visual abilities at the PRL. We also determined the neural correlates (3-Tesla fMRI) of learning success. Twelve MD patients (eight with age-related macular dystrophy, four with hereditary macular dystrophies) were trained on a texture discrimination task (TDT) over six days. Patients underwent three fMRI sessions (before, during and after training) while performing the TDT (target at PRL or opposite PRL). Reading speed, visual acuity (Vernier task) and contrast sensitivity were also assessed before and after training. With one exception, all patients showed improved performance (i.e. significant decrease in stimulus onset asynchronies and reaction times, significant increase in hit rates) on the TDT. Eight patients also showed moderate increases in reading speed, six patients showed improved thresholds in contrast sensitivity and nine patients showed improved thresholds in a vernier visual acuity task after TDT training. We found an increase in BOLD response in the projections zone of the PRL in the primary visual cortex in nine of twelve patients after training. The change in fMRI signal correlated (r = .8; p = .02) with the patients’ performance enhancements when the target was in the PRL. The results suggest that perceptual learning can enhance eccentric vision and cortical processing in MD patients.
PURPOSE
Reconstruction of x-ray computed tomography (CT) data remains a mathematically challenging problem in medical imaging. Complementing the standard analytical reconstruction methods, sparse regularization is growing in importance, as it allows inclusion of prior knowledge. The paper presents a method for sparse regularization based on the curvelet frame for the application to iterative reconstruction in x-ray computed tomography.
METHODS
In this work, the authors present an iterative reconstruction approach based on the alternating direction method of multipliers using curvelet sparse regularization.
RESULTS
Evaluation of the method is performed on a specifically crafted numerical phantom dataset to highlight the method's strengths. Additional evaluation is performed on two real datasets from commercial scanners with different noise characteristics, a clinical bone sample acquired in a micro-CT and a human abdomen scanned in a diagnostic CT. The results clearly illustrate that curvelet sparse regularization has characteristic strengths. In particular, it improves the restoration and resolution of highly directional, high contrast features with smooth contrast variations. The authors also compare this approach to the popular technique of total variation and to traditional filtered backprojection.
CONCLUSIONS
The authors conclude that curvelet sparse regularization is able to improve reconstruction quality by reducing noise while preserving highly directional features.
Differential phase contrast imaging (DPCI) enables the visualization of soft tissue contrast using X-rays. In this work we introduce a reconstruction framework based on curvelet expansion and sparse regularization for DPCI. We will show that curvelets provide a suitable data representation for DPCI reconstruction that allows preservation of edges as well as an exact analytic representation of the system matrix. As a first evaluation, we show results using simulated phantom data
This paper deals with a simulative approach for investigating the impact of inverter pulse inhibition during field weakening operation of permanent magnet synchronous machines and asynchronous induction machines. The modelling and simulation language Modelica is used to design models for the vehicle, the electric machine and the inverter in different abstraction levels. It is shown that when using a permanent magnet synchronous machine, a sudden breakdown of the field weakening current causes induction of high voltages in the stator windings. Hence, the electric system of the vehicle has to be protected against the resulting high currents. Furthermore precautions have to be taken to avoid dangerous driving conditions because of high braking torques in an inverter fault operation mode. In the case of the asynchronous induction machine an inverter pulse inhibition poses no big problem, neither for the electric system of the vehicle, nor the driver.
This paper focuses on simulation of permanent magnet synchronous machine (PMSM) with fractional-slot windings (FSW) in Modelica. Modeling of the electrical machines with object-oriented approach is shortly described, and a new Modelica library for simulation of electrical machines is introduced. The results of simulation of PMSMs with fractional slot windings are presented and explained. Special attention is paid to the higher harmonics and subharmonics produced by the winding and their influence on machine operation.
Condition monitoring of rotor problems such as demagnetization and eccentricity in permanent magnet synchronous motors (PMSM) is essential for guaranteeing high motor performance, efficiency, and reliability. However, there are many limitations to the off-line and on-line methods currently used for PMSM rotor quality assessment. In this paper, an inverter-embedded technique for automated detection and classification of PMSM rotor faults is proposed as an alternative. The main concept is to use the inverter to perform a test whenever the motor is stopped, to detect rotor faults independent of operating conditions or load torque oscillations, which is not possible with motor current signature analysis (MCSA). The d-axis is excited with a dc+ac signal, and the variation in the inductance pattern due to the change in the degree of magnetic saturation caused by demagnetization or eccentricity is observed for fault detection. An experimental study on a 7.5kW PMSM verifies that demagnetization and eccentricity can be detected and classified independent of the load with high sensitivity.
We propose a new framework for limited angle tomographic reconstruction. Our approach is based on the observation that for a given acquisition geometry only a few (visible) structures of the object can be reconstructed reliably using a limited angle data set. By formulating this problem in the curvelet domain, we can characterize those curvelet coefficients which correspond to visible structures in the image domain. The integration of this information into the formulation of the reconstruction problem leads to a considerable dimensionality reduction and yields a speedup of the corresponding reconstruction algorithms.
This paper presents a comprehensive computational fluid (CFD) model of a radial flux permanent magnet synchronous machine with interior magnets. In the CFD model the water jacket cooling and a simplified model of the topology of the distributed stator winding are considered. The heat sources of the CFD model are determined from a finite element analysis of the machine. The numerically determined temperature distributions of the machine are compared with measurement results from sensors located both in the stator and rotor. The particular focus of this paper is the analysis of the temperatures and the heat flow in the air gap and from the stator winding head and the rotor to the inner air.
The majority of the work performed for detecting eccentricity faults for permanent magnet synchronous motors (PMSM) focus on motor current signature analysis (MCSA), as it provides continuous on-line monitoring with existing current sensors. However, MCSA cannot be applied under nonstationary conditions and cannot distinguish faults with load torque oscillations, which are limitations for many PMSM drive applications. In this paper, it is shown that the d-axis inductance, L d , decreases with increase in the severity of eccentricity due to the change in the degree of magnetic saturation, and it is proposed as a new fault indicator. The inverter can be used to perform a standstill test automatically whenever the motor is stopped, to measure L d for eccentricity testing independent of load variations or oscillations, which is not possible with MCSA. An FE and experimental study on a 10hp PMSM verifies that eccentricity can be detected independent of the load with high sensitivity and reliability.
Innovative thermal model for the estimation of permanent magnet and stator winding temperatures
(2012)
In this paper an innovative thermal model for the determination of the temperatures of the permanent magnets and stator windings is presented. This model relies on one temperature sensor located in the stator core of the machine. The estimated stator winding and permanent magnet temperatures are determined by a simplified thermal lumped element network model with only two time constants. Due to the structure of the model and the measured stator core temperature the proposed thermal model is very robust. Distortion of the cooling circuit are inherently sensed such that the model can be used for the online prediction of temperatures. Experimental results based on an interior permanent magnet synchronous machine are presented to validate the presented model.
In this paper a new model for the estimation of the stator winding and rotor cage temperatures of induction machines is presented. This model can be used in series applications of machines operated under dynamic load conditions where stator and rotor temperature shall be monitored. The proposed model relies on a simplified lumped element thermal equivalent circuit model where the stator core temperature serves as input quantity. The great advantage of this model is that it covers ambient and cooling conditions inherently. This leads to great simplicity and robustness. The parametrization and validation of the model through experimental data is presented. Advantages, drawbacks and possible implementations are discussed.
Steigende Fallzahlen von Menschen mit Demenz, der komplexe Bedarf und eine unzureichende Versorgungsrealität erfordern innovative und nachhaltige Lösungsansätze. Der Beitrag stellt ein Versorgungsmodell für Menschen mit Demenz vor, das infolge akuter Versorgungsprobleme entwickelt wurde und das eine umfassende, patientenorientierte Versorgung über Sektorengrenzen hinweg umsetzt. Beschrieben werden Struktur und Bedingungen gelingender Kooperation und umfassender Versorgung.
In the literature, the effects caused by a single or several adjacently broken rotor bars, or a broken end ring are thoroughly investigated. The phenomenon of various nonadjacently broken rotor bars has not been studied so far in detail. Since nonadjacently broken rotor bars may give rise to fault signatures, which are not directly related with the fault extent, it is important to understand the nature of multiple electrical rotor asymmetries in induction machines. The purpose of this paper is thus to investigate several combinations of electrical rotor asymmetries, to systematically elaborate the phenomena related to the broken bars and end rings. In this paper, a sophisticated simulation model and measurement results are used to analyze the phenomenon rotor fault.
For a totally enclosed fan-cooled induction machine, two methods of numerical analysis are compared with measurements. The first numerical method is based on computational fluid dynamics (CFDs) and the second one uses a thermal equivalent circuit (TEC). For the analysis based on CFD, a 3-D induction machine including housing is modeled. The numeric solution of the flow equations is determined for stationary temperature distributions. For the TEC, a discretized one-and-a-half-dimensional model of the induction machine is considered. With the TEC model, stationary and transient operating conditions can be simulated. Measurement results are determined by iron-copper-nickel sensors embedded in the stator winding and the housing, as well as by an IR sensor for measuring the rotor temperature. With these measurement signals, stationary and transient operating conditions can be analyzed. For stationary operating conditions, additionally, the housing temperatures are determined by an IR camera. The investigated simulation and measurement methods reveal different local and global temperatures, and thus, only certain aspects and characteristics of the obtained temperatures can be compared. Nevertheless, certain conclusions can be drawn from comparing these aspects considering the actual restrictions of each of the applied methods.
We investigated the role of informative feedback on the neural correlates of perceptual learning in a coherent-motion detection paradigm. Stimulus displays consisted of four patches of moving dots briefly (500 ms) presented simultaneously, one patch in each visual quadrant. The coherence level was varied in the target patch from near threshold to high, while the other three patches contained only noise. The participants judged whether coherent motion was present or absent in the target patch. To guarantee central fixation, a secondary RSVP digit-detection task was performed at fixation. Over six training sessions subjects learned to detect coherent motion in a predefined quadrant (i.e., the learned location). Half of our subjects were randomly assigned to the feedback group, where they received informative feedback after each response during training, whereas the other group received non-informative feedback during training that a response button was pressed. We investigated whether the presence of informative feedback during training had an influence on the learning success and on the resulting BOLD response. Behavioral data of 24 subjects showed improved performance with increasing practice. Informative feedback promoted learning for motion displays with high coherence levels, whereas it had little effect on learning for displays with near-threshold coherence levels. Learning enhanced fMRI responses in early visual cortex and motion-sensitive area MT+ and these changes were most pronounced for high coherence levels. Activation in the insular and cingulate cortex was mainly influenced by coherence level and trained location. We conclude that feedback modulates behavioral performance and, to a lesser extent, brain activation in areas responsible for monitoring perceptual learning.
We consider the reconstruction problem for limited angle tomography using filtered backprojection (FBP) and lambda tomography. We use microlocal analysis to explain why the well-known streak artifacts are present at the end of the limited angular range. We explain how to mitigate the streaks and prove that our modified FBP and lambda operators are standard pseudodifferential operators, and so they do not add artifacts. We provide reconstructions to illustrate our mathematical results.
We investigate the reconstruction problem of limited angle tomography. Such problems arise naturally in applications like digital breast tomosynthesis, dental tomography, electron microscopy, etc. Since the acquired tomographic data is highly incomplete, the reconstruction problem is severely ill-posed and the traditional reconstruction methods, e.g. filtered backprojection (FBP), do not perform well in such situations.
To stabilize the reconstruction procedure additional prior knowledge about the unknown object has to be integrated into the reconstruction process. In this work, we propose the use of the sparse regularization technique in combination with curvelets. We argue that this technique gives rise to an edge-preserving reconstruction. Moreover, we show that the dimension of the problem can be significantly reduced in the curvelet domain. To this end, we give a characterization of the kernel of the limited angle Radon transform in terms of curvelets and derive a characterization of solutions obtained through curvelet sparse regularization. In numerical experiments, we will show that the theoretical results directly translate into practice and that the proposed method outperforms classical reconstructions.
Patients with juvenile retinal dystrophy often report that they are unaware of their central scotoma, suggesting the presence of perceptual filling-in. We used functional Magnetic Resonance Imaging (fMRI) to determine possible neural correlates of perceptual filling-in in patients with retinal distrophy and clinically established central scotoma in both eyes. The data of 5 patients (Stargardt disease, cone-rod dystrophy; mean age 45 yrs; scotoma diameter 10-20°) and of 5 normally sighted controls were analyzed. Fixation behaviour and perimetry were measured with a Nidek microperimeter. Magnetic resonance imaging was performed using a Siemens 3T Allegra scanner. We stimulated the central visual field (30 deg) with a vertically oriented, low spatial frequency (1 c/deg) high-contrast sinewave grating that was either a) continuous, or b) was interrupted by a central grey disk. The disk was either slightly larger than the scotoma (detectable on 75% of trials) or slightly smaller (detectable on 25% of trials). To control for attention, an eccentric fixation task was performed during scanning. Data were analyzed using SPM8 (GLM with ROI analysis to obtain percent signal change for foveal projection zone). Results: for all patients, the BOLD signal in the foveal projection area was significantly higher for the small disk (i.e., condition leading to complete filling-in) than for the large disk (i.e., no filling-in). This effect was absent in the control subjects. Our findings support the existence of an active neural process that leads to filling-in in patients with central visual field scotomata.
Artifacts in Incomplete Data Tomography with Applications to Photoacoustic Tomography and Sonar
(2015)
We develop a paradigm using microlocal analysis that allows one to characterize the visible and added singularities in a broad range of incomplete data tomography problems. We give precise characterizations for photoacoustic and thermoacoustic tomography and sonar, and provide artifact reduction strategies. In particular, our theorems show that it is better to arrange sonar detectors so that the boundary of the set of detectors does not have corners and is smooth. To illustrate our results, we provide reconstructions from synthetic spherical mean data as well as from experimental photoacoustic data.
We investigate the reconstruction problem for limited angle tomography. Such problems arise naturally in applications like digital breast tomosynthesis, dental tomography, etc. Since the acquired tomographic data is highly incomplete, the reconstruction problem is severely ill-posed and the traditional reconstruction methods, such as filtered backprojection (FBP), do not perform well in such situations. To stabilize the inversion we propose the use of a sparse regularization technique in combination with curvelets. We argue that this technique has the ability to preserve edges. As our main result, we present a characterization of the kernel of the limited angle Radon transform in terms of curvelets. Moreover, we characterize reconstructions which are obtained via curvelet sparse regularizations at a limited angular range. As a result, we show that the dimension of the limited angle problem can be significantly reduced in the curvelet domain.
In this paper, we study the effect of skill-biased technological change on unemployment and wage inequality in the presence of a link between social benefits and average income. In this case, an increase in the productivity of skilled workers and hence their wage leads to an increase in average income and hence in benefits. The increased fallback income, in turn, makes unskilled workers ask for higher wages. As higher wages are not justified by respective productivity increases, unemployment rises. More generally, we show that skill-biased technological change leads to increasing unemployment of the unskilled and to a moderately increasing wage inequality when benefits are endogenous. The model provides a theoretical explanation for diverging dynamics in wage inequality and unemployment under different social benefits regimes: Analyzing the social legislation in 14 countries, we find that benefits are linked to the evolution of average income in Continental Europe but not in the U.S. and the UK. Given this institutional difference, our model predicts that skill-biased technological change leads to rising unemployment in Continental Europe and rising wage inequality in the U.S. and the UK....
We propose a method for an efficient optimization of experimental designs, using a combination of discrete adjoint computations, Taylor arithmetic and matrix calculus. Compared to the state of the art of using finite differences or the forward mode of automatic differentiation, our proposed approach leads to a reduction of the relative temporal complexity from linear to constant time in the number of control variables and measurement weights. We demonstrate that the advantageous complexity results are not only of theoretical nature, but lead to significant speedups in practice as well. With our implementation we are very close to the theoretical bound of the cheap gradient principle. We present one academic (spatially discretized heat equation) and two industrial application examples (biochemical process/Diesel-oxidation catalysis process) where we achieve speedups that range between 10 and 100. In addition to our core results, we also describe an efficient adjoint approach for the treatment of differential algebraic equations and present adjoint formulas for constrained least-squares problems.
A Krylov subspace projection method which provides simultaneous solutions of the Helmholtz equation at multiple frequencies in one solution step is presented. The projector is obtained with an unsymmetric block Lanczos algorithm applied to a transfer function derived from a finite element discretization. This approach is equivalent to a matrix-valued Padé approximation of the transfer function. The proposed method is an extension of the formulation presented in [J. Comput. Acoust. 8 (2000) 223] to unsymmetric systems and allows the treatment of a much wider range of practical problems, including near-field and fluid–structure interaction computations
We propose a new algorithmic approach to the non-smooth and non-convex Potts problem (also called piecewise-constant Mumford–Shah problem) for inverse imaging problems. We derive a suitable splitting into specific subproblems that can all be solved efficiently. Our method does not require a priori knowledge on the gray levels nor on the number of segments of the reconstruction. Further, it avoids anisotropic artifacts such as geometric staircasing. We demonstrate the suitability of our method for joint image reconstruction and segmentation. We focus on Radon data, where we in particular consider limited data situations. For instance, our method is able to recover all segments of the Shepp–Logan phantom from seven angular views only. We illustrate the practical applicability on a real positron emission tomography dataset. As further applications, we consider spherical Radon data as well as blurred data.
Specific structural effects by strengthening pre-stressed concrete beams with externally bonded FRP
(2012)
In this contribution, a novel two-channel acoustic front-end for robust automatic speech recognition in adverse acoustic environments with nonstationary interference and reverberation is proposed. From a MISO system perspective, a statistically optimum source signal extraction scheme based on the multichannel Wiener filter (MWF) is discussed for application in noisy and underdetermined scenarios. For free-field and diffuse noise conditions, this optimum scheme reduces to a Delay & Sum beamformer followed by a single-channel Wiener postfilter. Scenarios with multiple simultaneously interfering sources and background noise are usually modeled by a diffuse noise field. However, in reality, the free-field assumption is very weak because of the reverberant nature of acoustic environments. Therefore, we propose to estimate this simplified MWF solution in each frequency bin separately to cope with reverberation. We show that this approach can very efficiently be realized by the combination of a blocking matrix based on semi-blind source separation (‘directional BSS’), which provides a continuously updated reference of all undesired noise and interference components separated from the desired source and its reflections, and a single-channel Wiener postfilter. Moreover, it is shown, how the obtained reference signal of all undesired components can efficiently be used to realize the Wiener postfilter, and at the same time, generalizes well-known postfilter realizations. The proposed front-end and its integration into an automatic speech recognition (ASR) system are analyzed and evaluated in noisy living-room-like environments according to the PASCAL CHiME challenge. A comparison to a simplified front-end based on a free-field assumption shows that the introduced system substantially improves the speech quality and the recognition performance under the considered adverse conditions.
The rising adoption of NoSQL technology in enterprises causes a heterogeneous landscape of different data stores. Different stores provide distinct advantages and disadvantages, making it necessary for enterprises to facilitate multiple systems for specific purposes. This resulting polyglot persistence is difficult to handle for developers since some data needs to be replicated and aggregated between different and within the same stores. Currently, there are no uniform tools to perform these data transformations since all stores feature different APIs and data models. In this paper, we present the transformation language NotaQL that allows cross-system data transformations. These transformations are output-oriented, meaning that the structure of a transformation script is similar to that of the output. Besides, we provide an aggregation-centric approach, which makes aggregation operations as easy as possible.
After leaving the lecture hall, students hardly reflect about the lesson's contents. To foster learning, the lesson content should be reorganised, elaborated and critically reflected. Tagging and blogging offer the opportunity to actively engage students in follow-up course work. This paper presents the results of a case study exploring the use of a social web application to encourage student contributions within the context of an undergraduate university seminar. The tagblog combines blogging, tagging and rating as three forms of online user contribution on Web 2.0 to develop a shared, emergent group knowledge repository. Blog posts, tags and comments are analysed to examine how user contributions reflect the active processing of learning content.
The method of loci is one, if not the most, efficient mnemonic encoding strategy. This spatial mnemonic combines the core cognitive processes commonly linked to medial temporal lobe (MTL) activity: spatial and associative memory processes. During such processes, fMRI studies consistently demonstrate MTL activity, while electrophysiological studies have emphasized the important role of theta oscillations (3–8 Hz) in the MTL. However, it is still unknown whether increases or decreases in theta power co-occur with increased BOLD signal in the MTL during memory encoding. To investigate this question, we recorded EEG and fMRI separately, while human participants used the spatial method of loci or the pegword method, a similarly associative but nonspatial mnemonic. The more effective spatial mnemonic induced a pronounced theta power decrease source localized to the left MTL compared with the nonspatial associative mnemonic strategy. This effect was mirrored by BOLD signal increases in the MTL. Successful encoding, irrespective of the strategy used, elicited decreases in left temporal theta power and increases in MTL BOLD activity. This pattern of results suggests a negative relationship between theta power and BOLD signal changes in the MTL during memory encoding and spatial processing. The findings extend the well known negative relation of alpha/beta oscillations and BOLD signals in the cortex to theta oscillations in the MTL.
Production supermarkets are a system to ensure high-frequency, demand-oriented, in-plant production supply with low lot sizes. The paper presents methods to design and dimension these systems using qualitative design criteria and quantitative approaches based on well-known analytical methods for conventional order picking systems. The methods help to design layout, provision type and path strategy and to derive space consumption and the number of supermarket employees required to fulfill a defined demand.
In-plant milk-run systems represent transportation systems, where materials are delivered from a central storage area to several points of use on fixed routes and in short and defined intervals. Milk-run systems generally enable frequent deliveries in low lot sizes with short lead times and low inventories at the points of use. Thus, stable and reliable system operation is crucial to avoid delays and material shortages. In industrial practice, milk-run trains usually share resources, for example, loading areas and technology and use the same tracks, leading to dependencies between routes and possible traffic jams and blockages, which significantly affect cycle times and may lead to instabilities in the system. We present a simulation model to analyse in-plant milk-run systems with a focus on typical traffic situations. We describe its application to a large industrial case study in detail and derive recommendations for designing routes with low risk of delays.
In-plant milk-run systems are transportation systems, where materials are delivered from a central storage area to several points of use on defined routes and in short intervals. Milk-run systems generally enable frequent deliveries in smaller lot sizes with short lead times and low inventory at the points of use. In real milk-run systems, the number of deliveries per interval varies due to, e.g., variations in the production program. To enable efficient and stable milk-run system operations, these systems therefore must be able to cope with peaks in the number of deliveries. We develop different strategies for handling these delivery peaks and evaluate them with respect to delivery cost, lead time and service level using real material consumption data from two large companies from the automotive industry.
New German guideline for strengthening concrete structures with adhesive bonded reinforcement
(2011)
Evaluation of the bond behavior at the intermediate crack element with a special test procedure
(2011)
The bond behavior of externally bonded reinforcement is one main parameter for the load bearing capacity of strengthened reinforced concrete structures. Because of the low bond force transfer at the outmost bending crack, the bond forces have also been transferred to elements between the cracks in order to effectively utilize the permissible stress of the CFRP strip. Two different experimental series on the bond behavior on the intermediate crack element were carried out. Small tests on an idealized intermediate crack element were made to evaluate the bond stress increase. In the second series parts of a structural element, which had the length of an intermediate crack element were tested to find out about other effects which only occur in the structural element. Based on these experimental data and the differential equation of the sway bond a new concept for the bond behavior of externally bonded reinforcement has been found out.
When reinforced concrete is strengthened by using externally bonded CFRP strips
it is not sufficient to perform checks just for the end anchorages. The bond forces must be transferred where the load is applied. These bond forces may be transferred by the elements in between flexural cracks which are mainly influenced by the bond behavior of the internal and external reinforcing. Therefore the models which were derived from end anchorage tests must be expanded to include other effects as well.
These models were first described theoretically by the differential equation of bond and a bilinear bond stress slippage relationship. To evaluate this theoretically derived relationship numerous experiments on the intermediate crack elements where conducted.
In this paper, an evaluation of these experiments in two different ways is shown. The one evaluation is carried out only on the fracture force, the other analysis also takes the slippage occurred in the intermediate crack element tests into account. With both evaluations it can be shown, that besides the previously scheduled bilinear bond approach additional friction effects between the already decoupled fracture surfaces of the bonded strips occur.
By enlarging the flexural capacity of reinforced concrete structures with externally bonded CRFP materials the shear behavior is also affected. To evaluate this effect shear tests on flexural strengthened reinforced concrete beams were carried out. Half of the tests were done with externally bonded CFRP-strips and the other half with near surface mounted reinforcement. Based on these tests and the EC2 approach for the shear force was checked.
First results regarding the realisation of multi-material components manufactured by Laser Beam Melting of polymers and metals are published. For realising composite structures from polymer powders by additive manufacturing, at first relevant material properties regarding compatibility have to be analysed. The paper shows the main requirements for compatibility between different materials and offers first results in form of a compatibility matrix of possible combinations for composite structures.For achieving gradient properties of additively manufactured metal parts by using composite materials the composition of alloying components in the powder and adapted process strategies are varied. As an alternative to atomizing pre-alloyed materials, mixtures of different powders are investigated.
By simultaneous laser beam melting (SLBM), different polymer powders can be processed to multi-material parts, which offers the potential to enlarge the field of application for conventional LBM. In a SLBM process, a powder bed consisting of different polymers and therefore with different melting and crystallization temperatures is deposited. Besides the use of infrared emitters for preheating the lower melting polymer, a CO2 laser distributes the necessary preheating temperature of the higher melting polymer. In the last step, a thulium fibre laser distributes the energy necessary for melting the two preheated powders simultaneously. In order to analyze the temperature gradients of the process on the powder surface and in deeper layers, a high-resolution thermal imaging system and thermocouples are used.
The generic REMOS (REverberation MOdeling for robust Speech recognition) concept is extended in this contribution to cope with additional noise components. REMOS originally embeds an explicit reverberation model into a hiddenMarkov model (HMM) leading to a relaxed conditional independence assumption for the observed feature vectors. During recognition, a nonlinear optimization problem is to be solved in order to adapt the HMMs' output probability density functions to the current reverberation conditions. The extension for additional noise components necessitates a modified numerical solver for the nonlinear optimization problem. We propose an approximation scheme based on continuous piecewise linear regression. Connected-digit recognition experiments demonstrate the potential of REMOS in reverberant and noisy environments. They furthermore reveal that the benefit of an explicit reverberation model, overcoming the conditional independence assumption, increases with increasing signal-to-noise-ratios.
Holding non-co-located conversationswhile driving is dangerous (Horrey and- Wickens, 2006; Strayer et al., 2006), much more so than conversations with physically present, “situated” interlocutors
(Drews et al., 2004). In-car dialogue systems typically resemble non-co-located conversations more, and share their negative impact (Strayer et al., 2013). We implemented and tested a simple strategy
for making in-car dialogue systems aware of the driving situation, by giving them the capability to interrupt themselves when a dangerous situation is detected,and resume when over. We show that this improves both driving performance and recall of system-presented information,
compared to a non-adaptive strategy.
When humans speak, they do not plan their full utterance inall detail before beginning to speak, nor do they speak piece-by-piece and ignoring their full message – instead humans usepartial representations in which they fill in the missing partsas the utterance unfolds. Incremental speech synthesizers, incontrast, have not yet made use of partial representations and theinformation contained there-in.We analyze the quality of prosodic parameter assignments(pitch and duration) generated from partial utterance specifi-cations (substituting defaults for missing features) in order todetermine the requirements that symbolic incremental prosodymodelling should meet. We find that broader, higher-level infor-mation helps to improve prosody even if lower-level informationabout the near future is yet unavailable. Furthermore, we findthat symbolic phrase-level or utterance-level information is mosthelpful towards the end of the phrase or utterance, respectively,that is, when this information is becoming available even in theincremental case. Thus, the negative impact of incremental pro-cessing can be minimized by using partial representations thatare filled in incrementally.
Automatic speech recognition (ASR) technology has been developed to such a level that off-the-shelf distributed speech recognition services are available (free of cost), which allow researchers to integrate speech into their applications with little development effort or expert knowledge leading to better results compared with previously used open-source tools.
Often, however, such services do not accept language models or grammars but process free speech from any domain. While results are very good given the enormous size of the search space, results frequently contain out-of-domain words or constructs that cannot be understood by subsequent domain-dependent natural language understanding (NLU) components. We present a versatile post-processing technique based on phonetic distance that integrates domain knowledge with open-domain ASR results, leading to improved ASR performance. Notably, our technique is able to make use of domain restrictions using various degrees of domain knowledge, ranging from pure vocabulary restrictions via grammars or N-Grams to restrictions of the acceptable utterances. We present results for a variety of corpora (mainly from human-robot interaction) where our combined approach significantly outperforms Google ASR as well as a plain open-source ASR solution.
Human speakers plan and deliver their utterances incrementally, piece-by-piece, and it is obvious that their choice regarding phonetic details (and the details' peculiarities) is rarely determined by globally optimal solutions. In contrast, parametric speech synthesizers use a full-utterance context when optimizing vocoding parameters and when determing HMM states. Apart from being cognitively implausible, this impedes incremental use-cases, where the future context is often at least partially unavailable. This paper investigates the `locality' of features in parametric speech synthesis voices and takes some missing steps towards better HMM state selection and prosody modelling for incremental speech synthesis.
It is established that driver distraction is the result of sharing cognitive resources between the primary task (driving) and any other secondary task. In the case of holding conversations, a human passenger who is aware of the driving conditions can choose to interrupt his speech in situations potentially requiring more attention from the driver, but in-car information systems typically do not exhibit such sensitivity. We have designed and tested such a system in a driving simulation environment. Unlike other systems, our system delivers information via speech (calendar entries with scheduled meetings) but is able to react to signals from the environment to interrupt when the driver needs to be fully attentive to the driving task and subsequently resume its delivery. Distraction is measured by a secondary short-term memory task. In both tasks, drivers perform significantly worse when the system does not adapt its speech, while they perform equally well to control conditions (no concurrent task) when the system intelligently interrupts and resumes.
When a passenger speaks to a driver, he or she is co-located with the driver, is generally aware of the situation, and can stop speaking to allow the driver to focus on the driving task. In-car dialogue systems ignore these important aspects, making them more distracting than even cell-phone conversations. We developed and tested a "situationally-aware" dialogue system that can interrupt its speech when a situation which requires more attention from the driver is detected, and can resume when driving conditions return to normal. Furthermore, our system allows driver-controlled resumption of interrupted speech via verbal or visual cues (head nods). Over two experiments, we found that the situationally-aware spoken dialogue system improves driving performance and attention to the speech content, while driver-controlled speech resumption does not hinder performance in either of these two tasks
Robots should appropriately give reasons for their actions
when these actions affect a human’s action or goal space. Communicating reasons may help the human understand the robot’s intents and may initiate joint action, i. e., accepting the robot’s goals and cooperating on the robot’s actions. However, to be efficient, the communication of reasons should be limited to the necessary rather than to completeness, conforming to the Gricean Maxim of Quantity. Furthermore, what is necessary only becomes apparent as the situation evolves and hence, for seamless interaction, ongoing utterances must be adapted as they happen. We present a system that flexibly gives reasons in a reduced setting in which the robot needs to intrude a human’s personal space in order to reach its goal.