Computationally efficient simulation in urban mechanised tunnelling based on multi-level BIM models

The design of complex underground infrastructure projects involves various empirical, analytical or numerical models with different levels of complexity. The use of simulation models in current state-of-the-art tunnel design process can be cumbersome when significant manual, time-consuming preparation, analysis and excessive computing resources are required. This paper addresses the challenges connected with minimising the user workload and computational time, as well as enabling real-time computations during the construction. To ensure a seamless workflow during design and to minimise the computation time of the analysis, we propose a novel concept for BIM-based numerical simulations, enabling the modelling of the tunnel advance on different levels of detail in terms of geometrical representation, material modelling and modelling of the advancement process. To ensure computational efficiency, the simulation software has been developed with special emphasis on efficient implementation, including parallelisation strategies on shared and distributed memory systems. For real-time on-demand calculations, simulation based meta models are integrated into the software platform. The components of the BIM-based multi-level simulation concept are described and evaluated in detail by means of representative numerical examples

Automatic Verification of Transactions on an Object-Oriented Database

In the context of the object-oriented data model, a compiletime approach is given that provides for a significant reduction of the amount of run-time transaction overhead due to integrity constraint checking. The higher-order logic Isabelle theorem prover is used to automatically prove which constraints might, or might not be violated by a given transaction in a manner analogous to the one used by Sheard and Stemple (1989) for the relational data model. A prototype transaction verification tool has been implemented, which automates the semantic mappings and generates proof goals for Isabelle. Test results are discussed to illustrate the effectiveness of our approach

Molecular Spin Qudits for Quantum Algorithms

Presently, one of the most ambitious technological goals is the development of devices working under the laws of quantum mechanics. One prominent target is the quantum computer, which would allow the processing of information at quantum level for purposes not achievable with even the most powerful computer resources. The large-scale implementation of quantum information would be a game changer for current technology, because it would allow unprecedented parallelised computation and secure encryption based on the principles of quantum superposition and entanglement. Currently, there are several physical platforms racing to achieve the level of performance required for the quantum hardware to step into the realm of practical quantum information applications. Several materials have been proposed to fulfil this task, ranging from quantum dots, Bose-Einstein condensates, spin impurities, superconducting circuits, molecules, amongst others. Magnetic molecules are among the list of promising building blocks, due to (i) their intrinsic monodispersity, (ii) discrete energy levels (iii) the possibility of chemical quantum state engineering, and (iv) their multilevel characteristics, leading to the so called Qudits (d > 2), amongst others. Herein we review how a molecular multilevel nuclear spin qubit (or qudit, where d = 4), known as TbPc2, gathers all the necessary requirements to perform as a molecular hardware platform with a first generation of molecular devices enabling even quantum algorithm operations.Comment: Chem. Soc. Rev., 2017, Advance Articl

Sketch-a-Net that Beats Humans

We propose a multi-scale multi-channel deep neural network framework that, for the first time, yields sketch recognition performance surpassing that of humans. Our superior performance is a result of explicitly embedding the unique characteristics of sketches in our model: (i) a network architecture designed for sketch rather than natural photo statistics, (ii) a multi-channel generalisation that encodes sequential ordering in the sketching process, and (iii) a multi-scale network ensemble with joint Bayesian fusion that accounts for the different levels of abstraction exhibited in free-hand sketches. We show that state-of-the-art deep networks specifically engineered for photos of natural objects fail to perform well on sketch recognition, regardless whether they are trained using photo or sketch. Our network on the other hand not only delivers the best performance on the largest human sketch dataset to date, but also is small in size making efficient training possible using just CPUs.Comment: Accepted to BMVC 2015 (oral

A method for the architectural design of distributed control systems for large, civil jet engines: a systems engineering approach

The design of distributed control systems (DCSs) for large, civil gas turbine engines is a complex architectural challenge. To date, the majority of research into DCSs has focused on the contributing technologies and high temperature electronics rather than the architecture of the system itself. This thesis proposes a method for the architectural design of distributed systems using a genetic algorithm to generate, evaluate and refine designs. The proposed designs are analysed for their architectural quality, lifecycle value and commercial benefit. The method is presented along with results proving the concept. Whilst the method described here is applied exclusively to Distributed Control System (DCS) for jet engines, the principles and methods could be adapted for a broad range of complex systems

Swarm Intelligence

Swarm Intelligence has emerged as one of the most studied artificial intelligence branches during the last decade, constituting the fastest growing stream in the bio-inspired computation community. A clear trend can be deduced analyzing some of the most renowned scientific databases available, showing that the interest aroused by this branch has increased at a notable pace in the last years. This book describes the prominent theories and recent developments of Swarm Intelligence methods, and their application in all fields covered by engineering. This book unleashes a great opportunity for researchers, lecturers, and practitioners interested in Swarm Intelligence, optimization problems, and artificial intelligence

Development of a new quantum trajectory molecular dynamics framework

An extension to the wave packet description of quantum plasmas is presented, where the wave packet can be elongated in arbitrary directions. A generalised Ewald summation is constructed for the wave packet models accounting for long-range Coulomb interactions and fermionic effects are approximated by purpose-built Pauli potentials, self-consistent with the wave packets used. We demonstrate its numerical implementation with good parallel support and close to linear scaling in particle number, used for comparisons with the more common wave packet employing isotropic states. Ground state and thermal properties are compared between the models with differences occurring primarily in the electronic subsystem. Especially, the electrical conductivity of dense hydrogen is investigated where a 15% increase in DC conductivity can be seen in our wave packet model compared to other models.Comment: 20 pages, 6 figure