Collision and Avoidance Modelling of Autonomous Vehicles using Genetic Algorithm and Neural Network

This thesis is to study the optimisation problems in autonomous vehicles, especially the modelling and optimisation of collision avoidance, and to develop some optimisation algorithms based on genetic algorithms and neural networks to operate autonomous vehicles without any collision. Autonomous vehicles, also called self-driving vehicles or driverless vehicles are completely robotised driving frameworks to allow the vehicle to react to outside conditions within a bunch of calculations to play out the undertakings. This thesis summarised artificial intelligence and optimisation techniques for autonomous driving systems in the literature. The optimisation problems related to autonomous vehicles are categorised into four groups: lane change, motion planner, collision avoidance, and artificial intelligence. A chart had been developed to summarise those research and related optimisation methods to help future researchers in the selection of optimisation methods Collision Avoidance is one of streamlining issues in autonomous vehicles. Several sensors had been used to identify position and dangers and collision avoidance algorithms had been developed to analyse the dangers and to use vehicles to avoid a collision. In this thesis, the current research on collision avoidance has been reviewed and some challenges and future works were presented to select the research direction of this thesis, the aim of this research will be the development of optimisation methods to avoid collisions in a predefined environment. The contributions of this thesis are that (1) a simulation model had been developed using Matlab for collision avoidance and serval scenarios were proposed and experimented with. The sensors are used as the inputs to determine collision in the learning preparation of the algorithm; (2) a neural network was used for collision avoidance of autonomous vehicles; (3) a new method was proposed with the combination of genetic algorithm and neural network. In the proposed frame, the neural network is used for decision making and a genetic algorithm is used for the training of the neural network. The results and experimentation show that the proposed strategies are well in the designed environment

Materials for Sustainable Nuclear Energy: A European Strategic Research and Innovation Agenda for All Reactor Generations

Nuclear energy is presently the single major low-carbon electricity source in Europe and is overall expected to maintain (perhaps eventually even increase) its current installed power from now to 2045. Long-term operation (LTO) is a reality in essentially all nuclear European countries, even when planning to phase out. New builds are planned. Moreover, several European countries, including non-nuclear or phasing out ones, have interests in next generation nuclear systems. In this framework, materials and material science play a crucial role towards safer, more efficient, more economical and overall more sustainable nuclear energy. This paper proposes a research agenda that combines modern digital technologies with materials science practices to pursue a change of paradigm that promotes innovation, equally serving the different nuclear energy interests and positions throughout Europe. This paper chooses to overview structural and fuel materials used in current generation reactors, as well as their wider spectrum for next generation reactors, summarising the relevant issues. Next, it describes the materials science approaches that are common to any nuclear materials (including classes that are not addressed here, such as concrete, polymers and functional materials), identifying for each of them a research agenda goal. It is concluded that among these goals are the development of structured materials qualification test-beds and materials acceleration platforms (MAPs) for materials that operate under harsh conditions. Another goal is the development of multi-parameter-based approaches for materials health monitoring based on different non-destructive examination and testing (NDE&T) techniques. Hybrid models that suitably combine physics-based and data-driven approaches for materials behaviour prediction can valuably support these developments, together with the creation and population of a centralised, “smart” database for nuclear materials

Deep language models for software testing and optimisation

Developing software is difficult. A challenging part of production development is ensuring programs are correct and fast, two properties satisfied with software testing and optimisation. While both tasks still rely on manual effort and expertise, the recent surge in software applications has led them to become tedious and time-consuming. Under this fast-pace environment, manual testing and optimisation hinders productivity significantly and leads to error-prone or sub-optimal programs that waste energy and lead users to frustration. In this thesis, we propose three novel approaches to automate software testing and optimisation with modern language models based on deep learning. In contrast to our methods, existing few techniques in these two domains have limited scalability and struggle when they face real-world applications. Our first contribution lies in the field of software testing and aims to automate the test oracle problem, which is the procedure of determining the correctness of test executions. The test oracle is still largely manual, relying on human experts. Automating the oracle is a non-trivial task that requires software specifications or derived information that are often too difficult to extract. We present the first application of deep language models over program execution traces to predict runtime correctness. Our technique classifies test executions of large-scale codebases used in production as “pass” or “fail”. Our proposed approach reduces by 86% the amount of test inputs an expert has to label by training only on 14% and classifying the rest automatically. Our next two contributions improve the effectiveness of compiler optimisation. Compilers optimise programs by applying heuristic-based transformations constructed by compiler engineers. Selecting the right transformations requires extensive knowledge of the compiler, the subject program and the target architecture. Predictive models have been successfully used to automate heuristics construction but their performance is hindered by a shortage of training benchmarks in quantity and feature diversity. Our next contributions address the scarcity of compiler benchmarks by generating human-likely synthetic programs to improve the performance of predictive models. Our second contribution is BENCHPRESS, the first steerable deep learning synthesizer for executable compiler benchmarks. BENCHPRESS produces human-like programs that compile at a rate of 87%. It targets parts of the feature space previously unreachable by other synthesizers, addressing the scarcity of high-quality training data for compilers. BENCHPRESS improves the performance of a device mapping predictive model by 50% when it introduces synthetic benchmarks into its training data. BENCHPRESS is restricted by a feature-agnostic synthesizer that requires thou sands of random inferences to select a few that target the desired features. Our third contribution addresses this inefficiency. We develop BENCHDIRECT, a directed language model for compiler benchmark generation. BENCHDIRECT synthesizes programs by jointly observing the source code context and the compiler features that are targeted. This enables efficient steerable generation on large scale tasks. Compared to BENCHPRESS, BENCHDIRECT matches successfully 1.8× more Rodinia target benchmarks, while it is up to 36% more accurate and up to 72% faster in targeting three different feature spaces for compilers. All three contributions demonstrate the exciting potential of deep learning and language models to simplify the testing of programs and the construction of better optimi sation heuristics for compilers. The outcomes of this thesis provides developers with tools to keep up with the rapidly evolving landscape of software engineering

Analyzing Granger causality in climate data with time series classification methods

Attribution studies in climate science aim for scientifically ascertaining the influence of climatic variations on natural or anthropogenic factors. Many of those studies adopt the concept of Granger causality to infer statistical cause-effect relationships, while utilizing traditional autoregressive models. In this article, we investigate the potential of state-of-the-art time series classification techniques to enhance causal inference in climate science. We conduct a comparative experimental study of different types of algorithms on a large test suite that comprises a unique collection of datasets from the area of climate-vegetation dynamics. The results indicate that specialized time series classification methods are able to improve existing inference procedures. Substantial differences are observed among the methods that were tested

The values of urban design - spatial models

Urban network morphometrics (UNeMos) is a research technique and a design decision aid in urban design. UNeMOS are network science-based configurational metrics of urban morphology that can inform urban designing decision-making, helping designers to discriminate between different 2D and 3D design options. However, some UNeMOS differ from the standard link/node network encoding by using a transport network’s specific encoding, thus lacking usability in mainstream transport and transport geography and analytical power in 3D. There is also a lack of comparison between these encodings and whether the transport geography combination of standard encoding/closeness centrality analysis using Euclidean, angular, or combination thereof are as discriminant or more of urban design network layout in 2D and 3D. The commentary addresses this research gap by reflecting on how the research original contributions reported in the collected publications have deployed diverse combinations of transport network encoding and spatial models of distance to evaluate the values of transport network configuration. The commentary critically contextualises the publications’ original contributions with reference to a leading research question and a sub-question: How well does UNeMOS, as a standard link/node spatial model and nonstandard spatial model, discriminate urban network configurations in 2D or 3D to capture urban design values? The publications cover urban morphology, form, property pricing, transport planning, spatial distribution, high-density city areas, urban design, and network analysis. The publications demonstrate a deep understanding of various aspects of intra-urban and urban studies, including historical morphological roots, challenges for future research, and their practical applications in urban design and planning. The methods employed in these studies involve a variety of quantitative and qualitative approaches. These include, among others, hedonic pricing modelling, multivariate models, road and metro network encoding, 2D and 3D spatial Design Network Analysis (sDNA) software, pedestrian standard path centre line network encoding, and value-based urban design. These methods have investigated the association between urban morphology, property prices, transport access, land-use resources, and pedestrian flows in contrasted urban contexts. The approaches in the publications demonstrate a comprehensive understanding of the complexities and interdependencies in intra-urban and urban studies. The research explores various spatial scales, from local urban design to macro-meso transport planning, and investigates the relationship between outdoor and indoor 3D pedestrian networks in high-density urban areas. Overall, the breadth and depth of the research in these publications and their original contributions showcase a strong foundation in intra-urban and urban studies, highlighting the importance of understanding urban environments’ spatial, socioeconomic, and morphological aspects for effective planning and design. Summary of the publications and contributions: Publication 1: Chiaradia, A., 2019. Urban Morphology/Urban Form. In: A. Orum, ed. The Wiley Blackwell Encyclopedia of Urban and Regional Studies. Hoboken, NJ: WileyBlackwell, pp. 1-6. The paper contextualises and traces succinctly, from 1830 to 2019, the historical roots of urban morphology, including street network focus. The article provides a general introduction to critical concepts. Space syntax is contextualised as performative urban morphology and referenced to the early work of Stübben (1911). The main contribution is the identification of three key challenges for future research: epistemological embedding, qualitative ontology, and a unified approach that bridges descriptive/explanatory and prescriptive/normative aspects. Publication 2: Chiaradia, A.*, Hillier, B., Schwander, C. and Barnes, Y., 2013. Compositional and urban form effects on residential property value patterns in Greater London. Proceedings of the Institution of Civil Engineers-Urban Design and Planning, 166(3), pp.176-199. This research used a hedonic pricing modelling framework. The road network encoding uses standard road centre line encoding transformed by space syntax software and centralities metrics quantitative spatial characterisation of road network shape/accessibility to investigate the association with property price of a large sample of adjacent properties (≈100,000). Findings are aligned with extant theory related to the hedonic modelling of the residential property price; dwelling size is the most important. The research reveals the importance of road network shape and accessibility characteristics in determining residential property prices in Greater London. The main contribution is the identification of two spatial scales associated with property prices: a local urban design scale (= 2,000 m). Publication 3: Chiaradia, A.*, Hillier, B., Schwander, C. and Wedderburn, M., 2012. Compositional and urban form effects on centres in Greater London. Proceedings of the Institution of Civil Engineers-Urban Design and Planning, 165(1), pp.21-42. This research used a multi-variate model, using standard road centre line encoding transformed by space syntax software and centralities metrics quantitative spatial characterisation of road network shape/accessibility and socio-economic variables to investigate the association with commercial rental values of a large sample of commercial property located in designated sub-centres. Findings show that a sub-centre can be spatially distinguished from its non-centre surroundings. A sub-centrality spatial signature: sub-centre spatial and socio-economic typology are identified. Of the two main space syntax spatial variables associated with the sub-centres signatures, one would be the remit or urban design (local spatial scale, walking scale <= 800 m) and the other (meso-scale, <= 2,000 m) would be the remit of transport planning. Publication 4: Zhang, L., Chiaradia, A.* & Zhuang, Y. A., 2015. Configurational Accessibility Study of Road and Metro Network in Shanghai. In: Q. Pan & J. Cao, eds. Recent Developments in Chinese Urban Planning. Heidelberg: Springer, pp. 219-245. This research deployed standard road centre line encoding, metro network topological encoding and 2D spatial Design Network Analysis (sDNA) software quantitative spatial characterisation of road network and metro network shape/accessibility to investigate the probability density function of spatial distribution of metro system access points, bus access points and commercial land use in a Mega City. The research shows the uneven spatial distribution of metro access points, bus access points, and commercial land use in Shanghai, with 60-70% associated with the top three deciles of road and metro network shape/accessibility. The main contribution is the comprehensive analysis of the spatial distribution of transport and land-use resources in a mega-city context. Publication 5: Zhang, L. & Chiaradia, A.*, 2019. Three-dimensional Spatial Network Analysis and Its Application in a High Density City Area, Central Hong Kong (In Chinese). Urban Planning International, 33(1), pp. 46-53. This research used 3D pedestrian standard path centre line network encoding and 3D sDNA software quantitative spatial characterisation of outdoor and indoor multi-level pedestrian network shape/accessibility to investigate their association with pedestrian flow level in one of the most complex multi-level-built environments. The research reveals a high association between the standard spatial characterisation of outdoor and indoor multi-level pedestrian network shape/accessibility and pedestrian flow levels in a complex built environment. The main contribution is the demonstration of the interdependence between outdoor and indoor pedestrian networks in a high-density urban context. Publication 6: Chiaradia, A.*, Sieh, L. and Plimmer, F., 2017. Values in urban design: A design studio teaching approach. Design Studies, 49, pp. 66-100. The paper refers to physical configurations in general and the movement network that UNeMos are measuring. It articulates a theoretical bridge between the technicalities of measuring urban morphology and the creative application of resulting insights about the impact of any proposed, designed urban shape on the performance of the urban ‘place’ of which it is a part. The basis of the bridge is the concept of value. This is not simply ‘price’ but an interdisciplinary social scientific compound construct inspired by an extensive anthropological meta-review of value: “that which matters, and the extent to which that matters.” The research establishes a theoretical bridge between urban morphology measurement and urban design creativity through the concept of value, which is adapted from Graeber’s general conceptualisation. The main contribution is developing a value-based approach to urban design, as demonstrated through the analysis of student work in an urban design studio. Publication 7: Chiaradia, A., Cooper, C., Webster, C., 2011, spatial Design Network Analysis Software, & Cooper, C.H. and Chiaradia, A.J., 2020. sDNA: 3D spatial network analysis for GIS, CAD, Command Line & Python. SoftwareX, 12, p.100525. Spatial Design Network Analysis (sDNA) is a toolbox for 2D and 3D spatial network analysis, especially street/path/urban network analysis, motivated by a need to use standard network links/nodes as the principal unit of analysis to analyse existing and projected network data. sDNA is usable from QGIS & ArcGIS geographic information systems, AutoCAD, Rhino Gh, and the command line via its own Python API. It computes measures of accessibility (reach, mean distance/closeness centrality, gravity), flows (bidirectional betweenness centrality) and efficiency (circuity) as well as convex hull properties, localised within lower- and upper-bounded radial bands. Weighting is flexible and can use geometric properties, data attached to links, zones, matrices or combinations of the above. Motivated by a desire to base network analysis on route choice and spatial cognition, distance can be network-Euclidean, angular, a mixture of both, custom, or specific to cyclists (avoiding slope and motorised traffic). In addition to statistics on network links, the following outputs can be computed: geodesics, network buffers, accessibility maps, convex hulls, flow bundles and skim matrices. Further tools assist with network preparation and calibration of network models to observed data. To date, sDNA has been used mainly for urban network analysis by academics and city planners/engineers for tasks including predicting pedestrian, cyclist, vehicle and metro flows and mode choice and quantifying the built environment for epidemiology and urban planning & design. The main contribution is developing a user-friendly and flexible software tool that supports various types of 3D network analysis, including accessibility, flows, efficiency measures, and various output formats and tools. The commentary critically introduces, compares, and analyses various spatial models of distance using the closeness centrality of a network, combinations of transport network encoding and topological, Euclidean, angular and hybrid distances for their capacity and limitations to discriminate transport network layout. It contextualised the issues related to how and what could be “counted so as to reveal the differences between one settlement structure and another?” (Hillier & Hanson, 1984) in 2D or 3D to capture urban design values. The main findings are as follows: • Topologic distance is inferior at measuring and discriminating distinct layout configurations of the transport networks. • To a very good extent, Euclidean distance measures and discriminates distinct layout configurations of transport networks, yet mainly grid-like layout. • Angular distance remedies the issues of Euclidean distance related to a deformed grid yet introduces errors that can be resolved by Hybrid distance. The link/node model of encoding transport network combined with closeness centrality of the network using spatial models of distance seems valid in discriminating distinct layout configurations of 2D and 3D transport networks. The publications’ original contributions demonstrate that these techniques empirically capture 2D and 3D urban design values

Systems Engineering: Availability and Reliability

Current trends in Industry 4.0 are largely related to issues of reliability and availability. As a result of these trends and the complexity of engineering systems, research and development in this area needs to focus on new solutions in the integration of intelligent machines or systems, with an emphasis on changes in production processes aimed at increasing production efficiency or equipment reliability. The emergence of innovative technologies and new business models based on innovation, cooperation networks, and the enhancement of endogenous resources is assumed to be a strong contribution to the development of competitive economies all around the world. Innovation and engineering, focused on sustainability, reliability, and availability of resources, have a key role in this context. The scope of this Special Issue is closely associated to that of the ICIE’2020 conference. This conference and journal’s Special Issue is to present current innovations and engineering achievements of top world scientists and industrial practitioners in the thematic areas related to reliability and risk assessment, innovations in maintenance strategies, production process scheduling, management and maintenance or systems analysis, simulation, design and modelling

The impact of arterial input function determination variations on prostate dynamic contrast-enhanced magnetic resonance imaging pharmacokinetic modeling: a multicenter data analysis challenge, part II

This multicenter study evaluated the effect of variations in arterial input function (AIF) determination on pharmacokinetic (PK) analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data using the shutter-speed model (SSM). Data acquired from eleven prostate cancer patients were shared among nine centers. Each center used a site-specific method to measure the individual AIF from each data set and submitted the results to the managing center. These AIFs, their reference tissue-adjusted variants, and a literature population-averaged AIF, were used by the managing center to perform SSM PK analysis to estimate Ktrans (volume transfer rate constant), ve (extravascular, extracellular volume fraction), kep (efflux rate constant), and τi (mean intracellular water lifetime). All other variables, including the definition of the tumor region of interest and precontrast T1 values, were kept the same to evaluate parameter variations caused by variations in only the AIF. Considerable PK parameter variations were observed with within-subject coefficient of variation (wCV) values of 0.58, 0.27, 0.42, and 0.24 for Ktrans, ve, kep, and τi, respectively, using the unadjusted AIFs. Use of the reference tissue-adjusted AIFs reduced variations in Ktrans and ve (wCV = 0.50 and 0.10, respectively), but had smaller effects on kep and τi (wCV = 0.39 and 0.22, respectively). kep is less sensitive to AIF variation than Ktrans, suggesting it may be a more robust imaging biomarker of prostate microvasculature. With low sensitivity to AIF uncertainty, the SSM-unique τi parameter may have advantages over the conventional PK parameters in a longitudinal study

Nuclear Power

At the onset of the 21st century, we are searching for reliable and sustainable energy sources that have a potential to support growing economies developing at accelerated growth rates, technology advances improving quality of life and becoming available to larger and larger populations. The quest for robust sustainable energy supplies meeting the above constraints leads us to the nuclear power technology. Today's nuclear reactors are safe and highly efficient energy systems that offer electricity and a multitude of co-generation energy products ranging from potable water to heat for industrial applications. Catastrophic earthquake and tsunami events in Japan resulted in the nuclear accident that forced us to rethink our approach to nuclear safety, requirements and facilitated growing interests in designs, which can withstand natural disasters and avoid catastrophic consequences. This book is one in a series of books on nuclear power published by InTech. It consists of ten chapters on system simulations and operational aspects. Our book does not aim at a complete coverage or a broad range. Instead, the included chapters shine light at existing challenges, solutions and approaches. Authors hope to share ideas and findings so that new ideas and directions can potentially be developed focusing on operational characteristics of nuclear power plants. The consistent thread throughout all chapters is the "system-thinking" approach synthesizing provided information and ideas. The book targets everyone with interests in system simulations and nuclear power operational aspects as its potential readership groups - students, researchers and practitioners

Nuclear Power - System Simulations and Operation

At the onset of the 21st century, we are searching for reliable and sustainable energy sources that have a potential to support growing economies developing at accelerated growth rates, technology advances improving quality of life and becoming available to larger and larger populations. The quest for robust sustainable energy supplies meeting the above constraints leads us to the nuclear power technology. Today's nuclear reactors are safe and highly efficient energy systems that offer electricity and a multitude of co-generation energy products ranging from potable water to heat for industrial applications. Catastrophic earthquake and tsunami events in Japan resulted in the nuclear accident that forced us to rethink our approach to nuclear safety, requirements and facilitated growing interests in designs, which can withstand natural disasters and avoid catastrophic consequences. This book is one in a series of books on nuclear power published by InTech. It consists of ten chapters on system simulations and operational aspects. Our book does not aim at a complete coverage or a broad range. Instead, the included chapters shine light at existing challenges, solutions and approaches. Authors hope to share ideas and findings so that new ideas and directions can potentially be developed focusing on operational characteristics of nuclear power plants. The consistent thread throughout all chapters is the system-thinking approach synthesizing provided information and ideas. The book targets everyone with interests in system simulations and nuclear power operational aspects as its potential readership groups - students, researchers and practitioners