IFC TO CITYGML CONVERSION ALGORITHM BASED ON GEOMETRY AND SEMANTIC MAPPING

Geographic information system (GIS) is known traditionally for the modelling of two-dimensional (2D) geospatial analysis and therefore present information about the extensive spatial framework. On the other hand, building information modelling (BIM) is digital representation of building life cycle. The increasing use of both BIM and GIS simultaneously because of their mutual relationship, as well as their similarities, has resulted in more relationships between both worlds, therefore the need for their integration. A significant purpose of these similarities is importing BIM data into GIS to significantly assist in different design-related issues. However, currently this is challenging due to the diversity between the two worlds which includes diversity in coordinate systems, three-dimensional (3D) geometry representation, and semantic mismatch. This paper describes an algorithm for the conversion of IFC data to CityGML in order to achieve the set goal of sharing information between BIM and GIS domains. The implementation of the programme developed using python was validated using an IFC model (block HO2) of a student’s hostel, Kolej Tun Fatima (KTF). The conversion is based on geometric and semantic information mapping and the use of 3D affine transformation of IFC data from local coordinate system (LCS) to CityGML world coordinate system (WCS) (EPSG:4236). In order to bridge the gap between the two data exchange formats of BIM and GIS, we conducted geometry and semantic mapping. In this paper, we limited the conversion of the IFC model on level of details 2 (LOD2). The conversion will serve as a bridge toward the development of a software that will perform the conversion to create a strong synergy between the two domains for purpose of sharing information.

Automatic cabin virtualization based on preliminary aircraft design data

Preliminary aircraft design and cabin design are essential and well-established steps within the product development cycle for modern passenger aircraft. In practice, the execution usually takes place sequentially, with the preliminary design defining a basic cabin layout and the detail implementation following in a subsequent step. To enable higher fidelity assessment of the cabin early in the design process—for example by means of virtual reality applications—this paper proposes an interface, which can derive detailed 3D geometry of the fuselage from preliminary design data provided in the Common Parametric Aircraft Configuration Schema (CPACS). This is a key step towards integration of cabin analysis and preliminary design in automated collaborative aircraft design chains, not only in terms of passenger comfort, but also manufacturability or crash safety. Like the TiGL Geometry Library for CPACS, the interface presented acts as a parameter engine, which translates data from CPACS into CAD geometry using the Open Cascade Technology library. However, the scope of TiGL is expanded significantly, albeit with an explicit focus on the fuselage, by including more details such as extruded frame and stringer profiles and floor structures. Furthermore, advanced knowledge management techniques are employed to detect and augment missing data. For virtual reality applications, triangulated representations of the CAD geometry can be provided in established exchange formats, creating an interface to common visualization platforms. Additionally, a new evolution of the cabin definition schema in CPACS is presented, to incorporate models of cabin components such as seats or sidewall panels enabling immersive virtual mock-ups.

Efficient simulation of 3D reaction-diffusion in models of neurons and networks

Neuronal activity is the result of both the electrophysiology and chemophysiology. A neuron can be well represented for the purposes of electrophysiological simulation as a tree composed of connected cylinders. This representation is also apt for 1D simulations of their chemophysiology, provided the spatial scale is larger than the diameter of the cylinders and there is radial symmetry. Higher dimensional simulation is necessary to accurately capture the dynamics when these criteria are not met, such as with wave curvature, spines, or diffusion near the soma. We have developed a solution to enable efficient finite volume method simulation of reaction-diffusion kinetics in intracellular 3D regions in neuron and network models and provide an implementation within the NEURON simulator. An accelerated version of the CTNG 3D reconstruction algorithm transforms morphologies suitable for ion-channel based simulations into consistent 3D voxelized regions. Kinetics are then solved using a parallel algorithm based on Douglas-Gunn that handles the irregular 3D geometry of a neuron; these kinetics are coupled to NEURON's 1D mechanisms for ion channels, synapses, etc. The 3D domain may cover the entire cell or selected regions of interest. Simulations with dendritic spines and of the soma reveal details of dynamics that would be missed in a pure 1D simulation. We describe and validate the methods and discuss their performance.

Discretization and degeometrization: A new relational quantum physics and an alternate path to quantum gravity

A novel quantum mechanical framework in discrete space and time leads to the derivation of the relativistic energy equation and a potential path toward quantum gravity. A unique approach to geometry is also derived where pregeometric points containing intrinsic compactified geometries generate a topologically 4D cosmos with a local dynamical 3D geometry. An interpretation of the quantum mechanics wavefunction is considered as longitudinal density waves traveling through dynamical pregeometric points. Similarly, general relativity can be described as density variations of the pregeometric points caused by matter-energy distribution. A primordial quantum (Higgs) field emerging at the big bang that generates all the other quantum fields is considered. A four-dimensional structure of black holes, and dark matter and energy are discussed.

Fabrication and First Full Characterisation of Timing Properties of 3D Diamond Detectors

Tracking detectors at future high luminosity hadron colliders are expected to be able to stand unprecedented levels of radiation as well as to efficiently reconstruct a huge number of tracks and primary vertices. To face the challenges posed by the radiation damage, new extremely radiation hard materials and sensor designs will be needed, while the track and vertex reconstruction problem can be significantly mitigated by the introduction of detectors with excellent timing capabilities. Indeed, the time coordinate provides extremely powerful information to disentangle overlapping tracks and hits in the harsh hadronic collision environment. Diamond 3D pixel sensors optimised for timing applications provide an appealing solution to the above problems as the 3D geometry enhances the already outstanding radiation hardness and allows to exploit the excellent timing properties of diamond. We report here the first full timing characterisation of 3D diamond sensors fabricated by electrode laser graphitisation in Florence. Results from a 270MeV pion beam test of a first prototype and from tests with a β source on a recently fabricated 55×55μm2 pitch sensor are discussed. First results on sensor simulation are also presented.

Comparison of the influence of 2D and 3D geometry of the main chamber on plasma parameters in the SOL of ASDEX Upgrade

In this paper the influence of toroidally asymmetric wall features on plasma solutions for ASDEX Upgrade is investigated by using the 3D scrape-off-layer simulation code EMC3-EIRENE. A comparison of simulation results in a 2D case with a toroidally symmetric first wall and divertor and a 3D case that differs from the 2D setup by including the 3D structure of the poloidal rib-limiters on the low field side of ASDEX Upgrade, highlights notable differences in the main chamber neutral particle distributions, ionisation sources and plasma flow patterns. Both neutral particle distribution and ionisation sources extend poloidally further upwards at the outer mid-plane in the 3D case and the plasma flow is globally influenced by the 3D wall features. Both simulations are conducted with identical input parameters to isolate the influence of wall geometry from other factors. By analysing the transport of neutrals from different poloidal locations it was possible to explain the observed discrepancies by different transport paths for recycled neutrals from the divertor region, only accessible in the 3D version of the wall geometry. Together with observed differences in fall-off lengths for plasma flow and electron temperature at the outer mid-plane, presented results are of key importance for interpreting global impurity migration experiments.

First structural observation around the hinge of the Mongolian Orocline (Central Asia): Implications for the geodynamics of oroclinal bending and the evolution of the Mongol-Okhotsk Ocean

To understand the origin of curved subduction zones has been one of the major challenges in plate tectonics. The Mongol-Okhotsk Orogen in Central Asia is characterized by the development of a U-shaped oroclinal structure that was accompanied by the continuous subduction of the Mongol-Okhotsk oceanic plate. Therefore, it provides a natural laboratory to understand why and how a subduction system became tightly curved. In this study, we provide the first structural observation around the hinge of the Mongolian Orocline (the Zag zone in Central Mongolia), with an aim to constrain the oroclinal geometry and to link hinge zone structures with the origin of the orocline. Our results show that rocks in the Zag zone are characterized by the occurrence of a penetrative foliation that is commonly subparallel to bedding. Both bedding and dominant fabric in the Zag zone are steeply dipping, and their strike orientations in a map view follow a simple curve around the hinge of the Mongolian Orocline, thus providing the first structural constraint for 3D geometry of the orocline. A secondary penetrative fabric parallel to the axial plane of the orocline was not observed, indicating a low degree of orogen-parallel shortening during oroclinal bending. Combining with available geological and geophysical data, we conclude that the Mongolian Orocline was developed in a period of Permian to Jurassic, and its origin was linked to the subduction of the Mongol-Okhotsk oceanic slab. We consider that the low-strain oroclinal bending likely resulted from the along-strike variation in trench retreat, which was either triggered by the negative buoyancy of the Mongol-Okhotsk oceanic slab, or driven by the relative rotation of the Siberian and North China cratons. Our results shed a light on 3D geometry and geodynamic mechanisms of large-scale oroclinal bending in an accretionary orogen.

Face Swapping Consistency Transfer with Neural Identity Carrier

Deepfake aims to swap a face of an image with someone else’s likeness in a reasonable manner. Existing methods usually perform deepfake frame by frame, thus ignoring video consistency and producing incoherent results. To address such a problem, we propose a novel framework Neural Identity Carrier (NICe), which learns identity transformation from an arbitrary face-swapping proxy via a U-Net. By modeling the incoherence between frames as noise, NICe naturally suppresses its disturbance and preserves primary identity information. Concretely, NICe inputs the original frame and learns transformation supervised by swapped pseudo labels. As the temporal incoherence has an uncertain or stochastic pattern, NICe can filter out such outliers and well maintain the target content by uncertainty prediction. With the predicted temporally stable appearance, NICe enhances its details by constraining 3D geometry consistency, making NICe learn fine-grained facial structure across the poses. In this way, NICe guarantees the temporal stableness of deepfake approaches and predicts detailed results against over-smoothness. Extensive experiments on benchmarks demonstrate that NICe significantly improves the quality of existing deepfake methods on video-level. Besides, data generated by our methods can benefit video-level deepfake detection methods.