21 research outputs found
Perceiving Mass in Mixed Reality through Pseudo-Haptic Rendering of Newton's Third Law
In mixed reality, real objects can be used to interact with virtual objects.
However, unlike in the real world, real objects do not encounter any opposite
reaction force when pushing against virtual objects. The lack of reaction force
during manipulation prevents users from perceiving the mass of virtual objects.
Although this could be addressed by equipping real objects with force-feedback
devices, such a solution remains complex and impractical.In this work, we
present a technique to produce an illusion of mass without any active
force-feedback mechanism. This is achieved by simulating the effects of this
reaction force in a purely visual way. A first study demonstrates that our
technique indeed allows users to differentiate light virtual objects from heavy
virtual objects. In addition, it shows that the illusion is immediately
effective, with no prior training. In a second study, we measure the lowest
mass difference (JND) that can be perceived with this technique. The
effectiveness and ease of implementation of our solution provides an
opportunity to enhance mixed reality interaction at no additional cost
A dynamic mode decomposition approach for large and arbitrarily sampled systems
Detection of coherent structures is of crucial importance for understanding the dynamics of a fluid flow. In this regard, the recently introduced Dynamic Mode Decomposition (DMD) has raised an increasing interest in the community. It allows to efficiently determine the dominant spatial modes, and their associated growth rate andfrequencyintime,responsiblefordescribingthetime-evolutionofanobservation ofthephysicalsystemathand.However,theunderlyingalgorithmrequiresuniformly sampled and time-resolved data, which may limit its usability in practical situations. Further, the computational cost associated with the DMD analysis of a large dataset is high, both in terms of central processing unit and memory. In this contribution, we present an alternative algorithm to achieve this decomposition, overcoming the above-mentioned limitations. A synthetic case, a two-dimensional restriction of an experimental flow over an open cavity, and a large-scale three-dimensional simulation, provide examples to illustrate the method
Irregular dynamics of cellular blood flow in a model microvessel
The flow of red blood cells within cylindrical vessels is complex and irregular, so long as the vessel diameter is somewhat larger than the nominal cell size. Long-time-series simulations, in which cells flow 105 vessel diameters, are used to characterize the chaotic kinematics, particularly to inform reduced-order models. The simulation model used includes full coupling between the elastic red blood cell membranes and surrounding viscous fluid, providing a faithful representation of the cell-scale dynamics. Results show that the flow has neither classifiable recurrent features nor a dominant frequency. Instead, its kinematics are sensitive to the initial flow configuration in a way consistent with chaos and Lagrangian turbulence. Phase-space reconstructions show that a low-dimensional attractor does not exist, so the observed long-time dynamics are effectively stochastic. Based on this, a simple Markov chain model for the dynamics is introduced and shown to reproduce the statistics of the cell positions
Investigating mode competition and three-dimensional features from two-dimensional velocity fields in an open cavity flow by modal decompositions
Shear-layer driven open cavity ïŹows are known to exhibit strong self-sustained oscillations of the shear-layer. Over some range of the control parameters, a competition between two modes of oscillations of the shear layer can occur. We apply both Proper Orthogonal Decomposition and Dynamic Mode Decomposition to experimental two-dimensional two-components time and spaced velocity ïŹelds of an incompressible open cavity ïŹow, in a regime of mode competition. We show that, although proper orthogonal decomposition successes in identifying salient features of the ïŹow, it fails at identifying the spatial coherent structures associated with dominant frequencies of the shear-layer oscillations. On the contrary, we show that, as dynamic mode decomposition is devoted to identify spatial coherent structures associated with clearly deïŹned frequency channels, it is well suited for investigating coherentstructuresinintermittentregimes.WeconsiderthevelocitydivergenceïŹeld, inordertoidentifyspanwisecoherentfeaturesoftheïŹow.Finally,weshowthatboth coherent structures in the inner-ïŹow and in the shear-layer exhibit strong spanwise velocitygradients,andarethereforethree-dimensiona
A comprehensive and policy-oriented model of the hydrogen vehicle fleet composition, applied to the UK market
Road vehicles play an important role in the UKâs energy systems and are a critical component in reducing the reliance on fossil fuels and mitigating emissions. A dynamic model of light-duty vehicle fleet, based on predator-prey concepts, is presented. This model is designed to be comprehensive but captures the important features of the competition between types of vehicles on the car market. It allows to predict the evolution of the hydrogen based vehicleâs role in the UKâs vehicle fleet. The model allows to forecast effects of policies, hence to inform policy makers. In particular, it is shown that the transition happens only if the hydrogen supply can absorb at least 350,000 new vehicles per year. In addition to this, the model is used to predict the demand for hydrogen for the passenger vehicle fleet for various scenarios. A key finding of the policy-oriented model is that a successful transition to a clean fleet before 2050 is unlikely without policies designed to fully support the supply chain development. It also shows that the amount of hydrogen required to support a full hydrogen based vehicle fleet is currently not economically viable; the needed infrastructure requires yearly investment larger than ÂŁ2.5 billions. In order to mitigate these costs, the policy focus should shift from hydrogen based vehicles to hybrid vehicles and range extenders in the transport energy system
CS-Embed at SemEval-2020 Task 9: The effectiveness of code-switched word embeddings for sentiment analysis
The growing popularity and applications of sentiment analysis of social media
posts has naturally led to sentiment analysis of posts written in multiple
languages, a practice known as code-switching. While recent research into
code-switched posts has focused on the use of multilingual word embeddings,
these embeddings were not trained on code-switched data. In this work, we
present word-embeddings trained on code-switched tweets, specifically those
that make use of Spanish and English, known as Spanglish. We explore the
embedding space to discover how they capture the meanings of words in both
languages. We test the effectiveness of these embeddings by participating in
SemEval 2020 Task 9: ~\emph{Sentiment Analysis on Code-Mixed Social Media
Text}. We utilised them to train a sentiment classifier that achieves an F-1
score of 0.722. This is higher than the baseline for the competition of 0.656,
with our team (codalab username \emph{francesita}) ranking 14 out of 29
participating teams, beating the baseline.Comment: Accepted at SemEval-2020, COLIN
Enhanced Data-driven LoRa LP-WAN Channel Model in Birmingham
Innovative solutions providing better coverage and minimized power consumption by end nodes such as Low Power Wide Area Networks (LP-WAN) have facilitated the advances towards improved IoT connectivity. Long Range Wide Area Net-work (LoRaWAN) technology stands out as one leading platform of LP-WANs receiving vast attention from both industry and academia. Performance evaluation of LoRaWAN is promising, in particular in the field of outdoor localization and object tracking. Limitations of node ranging and tracking without the need of energy-draining solutions like GPS, however, has not been tackled thoroughly. In this work, we explore the performance of the LoRa LP-WAN technology using real-life measurements in Birmingham, UK, using commercially available equipment. We present a channel attenuation model that can be utilized to estimate the path loss in 868 MHz ISM band in urban-similar areas. The proposed channel model is then compared to previously well-identified empirical path loss models and enhanced by detecting and eliminating outlier data from the obtained real measurements for an optimal fitted model. We, further, propose a novel RSSI distribution-based and k-means clustering to enhance the power-to-distance prediction accuracy that improves absolute errors by 4% and 18%
Pertinence des champs bidimensionnels dans l'analyse des phénomÚnes instationnaires tridimensionnels
Pertinence des champs bidimensionnels dans lâanalyse des phĂ©nomĂšnes instationnaires tridimensionnels. F. Lusseyran, J. Basley, F. Gueniat , L. Pastur Lâidentification de structures cohĂ©rentes dans les Ă©coulements de fluide constitue lâun des objectifs de nombreuses Ă©tudes actuelles en mĂ©canique des fluides. LâĂ©valuation de la cohĂ©rence spatiale a Ă©tĂ© longtemps rĂ©servĂ©e Ă lâapproche numĂ©rique, lâexpĂ©rimentation devant se limiter Ă des corrĂ©lations temporelles du fait des moyens mĂ©trologiques disponibles. Depuis 20 ans le dĂ©veloppement des techniques de vĂ©locimĂ©trie par images de particules (PIV) donne accĂšs Ă des champs de vitesse tout dâabord bidimensionnels (2D) et bicomposantes coplanaires (2C), pour actuellement aborder la mesure de champs tridimensionnels complets (3D,3C). Cette Ă©volution est motivĂ©e par le caractĂšre le plus souvent intrinsĂšquement 3D des tourbillons structurant la dynamique spatio-temporelle des sillages, des jets, des Ă©coulements impactant ou mĂȘme des couches limites et des couches de mĂ©langes. Cependant, les contraintes et les limites imposĂ©es par les techniques 3D, justifient encore largement lâexploration 2D. Dans cet exposĂ© nous abordons la validitĂ© et les possibilitĂ©s offertes par diffĂ©rentes dĂ©compositions modales des itĂ©rĂ©s 2D dâun champ de vitesse, rĂ©solus en temps (ou non rĂ©solus), prĂ©levĂ©s expĂ©rimentalement ou numĂ©riquement dans un champ de vitesse 3D fortement instationnaire. Trois dĂ©compositions modales sont appliquĂ©es Ă lâĂ©tude dâun Ă©coulement de rĂ©fĂ©rence, constituĂ© par une cavitĂ© ouverte en interaction avec une couche limite laminaire : - la dĂ©composition en modes propres orthogonaux (POD), la dĂ©composition en modes de Fourier globaux, la dĂ©composition en modes dynamiques (DMD). De plus, on peut ajouter aux propriĂ©tĂ©s propres Ă chacune de ces dĂ©compositions modales des propriĂ©tĂ©s physiques, comme lâincompressibilitĂ© (transmise aux modes spatiaux) ou la propagation non dispersive de modes transverses au plan de mesure. Lâinformation apportĂ©e par lâapproche 2D permet alors une incursion pertinente dans la troisiĂšme dimension
Analysis of Locally Coupled 3D Manipulation Mappings Based on Mobile Device Motion
We examine a class of techniques for 3D object manipulation on mobile devices, in which the device's physical motion is applied to 3D objects displayed on the device itself. This "local coupling" between input and display creates specific challenges compared to manipulation techniques designed for monitor-based or immersive virtual environments. Our work focuses specifically on the mapping between device motion and object motion. We review existing manipulation techniques and introduce a formal description of the main mappings under a common notation. Based on this notation, we analyze these mappings and their properties in order to answer crucial usability questions. We first investigate how the 3D objects should move on the screen, since the screen also moves with the mobile device during manipulation. We then investigate the effects of a limited range of manipulation and present a number of solutions to overcome this constraint. This work provides a theoretical framework to better understand the properties of locally-coupled 3D manipulation mappings based on mobile device motion
A statistical learning strategy for closed-loop control of fluid flows
This work discusses a closed-loop control strategy for complex systems utilizing scarce and streaming data. A discrete embedding space is first built using hash functions applied to the sensor measurements from which a Markov process model is derived, approximating the complex systemâs dynamics. A control strategy is then learned using reinforcement learning once rewards relevant with respect to the control objective are identified. This method is designed for experimental configurations, requiring no computations nor prior knowledge of the system, and enjoys intrinsic robustness. It is illustrated on two systems: the control of the transitions of a Lorenzâ63 dynamical system, and the control of the drag of a cylinder flow. The method is shown to perform well