Novel Multimodal Feedback Techniques for In-Car Mid-Air Gesture Interaction

This paper presents an investigation into the effects of different feedback modalities on mid-air gesture interaction for infotainment systems in cars. Car crashes and near-crash events are most commonly caused by driver distraction. Mid-air interaction is a way of reducing driver distraction by reducing visual demand from infotainment. Despite a range of available modalities, feedback in mid-air gesture systems is generally provided through visual displays. We conducted a simulated driving study to investigate how different types of multimodal feedback can support in-air gestures. The effects of different feedback modalities on eye gaze behaviour, and the driving and gesturing tasks are considered. We found that feedback modality influenced gesturing behaviour. However, drivers corrected falsely executed gestures more often in non-visual conditions. Our findings show that non-visual feedback can reduce visual distraction significantl

A full factorial numerical investigation and validation of precision end milling process for hardened tool steel

Tool steel materials have poor machinability, as the high hardness of the material will cause high cutting forces, premature failure of the cutting tools, and is also associated with machining induced tensile stresses within the work piece. Due to high experimental costs, there is no recent research on end milling tool steel, using full factorial experimental or numerical design. A 3D FE-model of a precision end milling process with a two flute ball nose cutter were established in this paper. The FE-Model used a subroutine to model hardening realised through the Johnson-Cook model, additionally were a material removal criteria developed and implemented. Through full factorial numerical simulations the influence of cutting parameters on cutting force of H13 tool steel was studied. Depth of cut was found to be the most influential machining parameter on cutting forces followed by feed rate and surface speed. Four milling experiments were carried out to validate the simulation results. It was found that the simulation and the experiments had a good agreement on the cutting forces. The validated FEA model can be used for further studies on residual stress or temperatures and to optimise the cutting process

Prediction of residual stress in precision milling of AISI H13 steel

Surface integrity describes the attributes of a surface and it influences the functional performance of a work piece significantly. Residual stress is one of the major characterization parameters of surface integrity. Non-favorable residual stresses on a machined surface can reduce the fatigue life and performance of the machined part. It therefore requires a prediction model for residual stress in order to establish machining strategy to obtain favorable residual stress for prolonged fatigue life. Hardened tool steels have been widely used to make molds and dies by precision milling in aerospace and automotive industries. Knowledge of the relationship between residual stress on the machined molds and machining conditions is very important for process control. In this work, a prediction model for residual stress was developed by using a model-based approach on an Artificial Neural Network. This model is expected to predict the residual stress based on cutting parameters such as cutting speed, feed rate, depth of cut and tool lead angle. Several precision milling trials were carried out using a central composite design method. The networks have been trained and validated by experimental results. The performance of a feed forward neural network model with backpropagation was assessed and compared with a radial basis function network model by criterion of least mean squared error. Furthermore, the neural network prediction model was supported by the finite element simulation of the milling process to understand the formation mechanism of the residual stress in the machined surface. It was found, that the predicted values by the neural network model matched well with the experimental results. The radial basis function network showed better results than the feed forward network and was therefore chosen to take forward in the analysis. The feed rate was in this case the most influential factor, because it contributes significantly to heat and deformation on the work piece. The model could be used to optimize machining processes to obtain machining strategy for generating favorable residual stress and increasing fatigue life performance of the machined parts

Numerical investigation of mechanical induced stress during precision end milling hardened tool steel

Hardened tool steels are widely used materials for forming dies, due to their increased strength and hardness. However, their machinability is very poor, due to the high hardness of the material, which leads to high cutting forces and premature failure of the cutting tools. This is also associated with machining induced tensile stresses within the work piece. No full factorial design has been performed when end milling tool steel, due to the high associated costs. Instead of physical experiments, numerical models are commonly used to save cost and time. However, most of the recent research focus was only on 2D FE-Models. 2D model can be used for simulation of some simplified process, however, the results are not sufficient for accurate prediction. Therefore, a 3D FE-model of a precision end milling process with a two-flute ball nose cutter were established in this paper, in order to build a multi cutting edge model. In the FE-Model, a subroutine was implemented to model work piece hardening during the cutting process. The subroutine realised an accurate prediction of the residual stress and cutting forces. In addition, a material removal criterion was developed and implemented. The influence of cutting parameters on cutting force for end milling H13 tool steel was studied, through full factorial numerical simulations, to evaluate the effectiveness of this FEA model. Subsequently, after validation of the FEM model through machining trials, empirical models were developed for predicting cutting forces and residual stress. The cutting parameters evaluated were cutting speed, feed rate and depth of cut. In summary, it was found that the simulation and the experiments had a good agreement on the value and trend of the residual stress. The FEM model can be effectively used to predict residual stress in the machined surface

A consensus map of QTLs controlling the root length of maize

Traits related to the root length of maize (Zea mays L.), reported by 15 QTL studies of nine mapping populations, were subjected to a QTL meta-analysis. Traits were grouped according to ontology, and we propose a system of abbreviations to unambiguously identify the different root types and branching orders. The nine maps were merged into a consensus map, and the number and positions of putative QTL clusters (MQTLs) were determined. A total of 161 QTLs was grouped into 24 MQTLs and 16 individual QTLs. Seven MQTLs harbored root traits, which had been reported to be collocated with QTLs for grain yield or other drought-responsive traits in the field. The most consistent collocations were observed for the number and weight of the seminal roots (five loci). Based on our analysis at least six loci are good candidates for further evaluation (bins 1.07, 2.04, 2.08, 3.06, 6.05 and 7.04). For example, the MQTL in bin 2.04 harbored ten different single QTLs; the MQTLs in bins 1.07 and 3.06 combined 11 and 7 QTLs, respectively, that were detected in more than three populations. The presented database is a first step for a comprehensive overview of the genetic architecture of root system architecture and its ecophysiological functio

Multidisziplinäre Simulation des Wirbelschleppen Durchfluges eines Flugzeuges mit dem DLR TAU-Code

Ausgangssituation: Für die Auslegung eines Flugzeuges sind eine Vielzahl unterschiedlicher Lastfälle zu berücksichtigen. Auf der einen Seite wird das Flugzeug für den Reiseflug optimiert, um eine möglichst große Reichweite bei geringem Brennstoffverbrauch zu erzielen. Auf der anderen Seite muss sichergestellt werden, dass ein Flugzeug auch in kritischen Situationen, wie beispielsweise der Begegnung mit einer kräftigen Böe oder der Wirbelschleppe eines voreilenden Flugzeuges, beherrschbar ist und den zusätzlichen Belastungen standhält. Um die zusätzlichen aerodynamischen Lasten vorherzusagen, werden heute in der Regel vereinfachte Methoden basierend auf Streifentheorie oder Doublet-Lattice-Methoden verwendet. Dadurch sind insbesondere bei hohen Fluggeschwindigkeiten (Kompressibilitätseffekte, Nichtlinearitäten) Vorhersagefehler der einfachen Methoden zu erwarten, weshalb entsprechend hohe Sicherheitsfaktoren aufgeschlagen werden. Das führt unter Umständen zu einer deutlichen Überdimensionierung der Struktur, und damit zu einem erhöhten Flugzeuggewicht. Ziel: Um die Genauigkeit bei der Vorhersage der zusätzlichen durch Wirbelschleppen induzierten Lasten gegenüber oben angesprochenen einfachen Verfahren zu verbessern, soll im DLR RANS-Löser TAU die Möglichkeit geschaffen werden, Wirbelschleppen-Begegnungen von Flugzeugen zu simulieren. Dabei soll auch die Reaktion des Flugzeuges in Folge der Lasten durch Kopplung zur Flugmechanik Berücksichtigung finden. Lösungsweg: Verschiedene Autoren haben in Euler- bzw. RANS-Verfahren den sogenannten Störgeschwindigkeitsansatz implementiert, bei dem die durch Böen induzierten Störungen in Form von Störgeschwindigkeiten als Funktion vom Raum und der Zeit vorgegeben werden können. Von Vorteil ist, das die atmosphärischen Störungen in der Simulation im Strömungsfeld nicht numerisch aufgelöst werden müssen. Es können Standardnetze Verwendung finden, was gegenüber der Auflösung der atmosphärischen Störungen eine effiziente numerische Behandlung verspricht. Dieses Verfahren ist für Böen-Begegnungen auch im TAU-Code implementiert und erfolgreich eingesetzt worden. Inzwischen ist es für Wirbelschleppenbegegnungen erweitert worden. Die durch die Wirbelschleppe induzierten Geschwindigkeiten werden durch Überlagerung zweier gegenläufiger „Burnham-Halloc“ Wirbel modelliert. Als Beispiel für einen Wirbelschleppen-Durchflug wurde die Interaktion eines generischen Kampfflugzeuges mit einer Wirbelschleppe eines voraus fliegenden Flugzeuges erfolgreich demonstriert. Neben der Aerodynamik wird auch die Flugmechanik berücksichtigt, um die Reaktion des Flugzeuges in Folge der Wirbelschleppe und von Steuerbewegungen zu erfassen

A rotation-equivariant convolutional neural network model of primary visual cortex

Classical models describe primary visual cortex (V1) as a filter bank of orientation-selective linear-nonlinear (LN) or energy models, but these models fail to predict neural responses to natural stimuli accurately. Recent work shows that models based on convolutional neural networks (CNNs) lead to much more accurate predictions, but it remains unclear which features are extracted by V1 neurons beyond orientation selectivity and phase invariance. Here we work towards systematically studying V1 computations by categorizing neurons into groups that perform similar computations. We present a framework to identify common features independent of individual neurons' orientation selectivity by using a rotation-equivariant convolutional neural network, which automatically extracts every feature at multiple different orientations. We fit this model to responses of a population of 6000 neurons to natural images recorded in mouse primary visual cortex using two-photon imaging. We show that our rotation-equivariant network not only outperforms a regular CNN with the same number of feature maps, but also reveals a number of common features shared by many V1 neurons, which deviate from the typical textbook idea of V1 as a bank of Gabor filters. Our findings are a first step towards a powerful new tool to study the nonlinear computations in V1