The Future of Central European Cities – Optimization of a Cellular Automaton for the Spatially Explicit Prediction of Urban Sprawl

The quantitative and qualitative measurement, prediction and evaluation of urban sprawl have come to play a central role in land-system science. One of the most important and most implemented artificial intelligence (AI) techniques in terms of urban systems simulation is cellular automata (CA) like SLEUTH. SLEUTH models the physical urban expansion by accomplishing four simple growth rules with every modeling step. Simultaneously, SLEUTH also reflects main drawbacks of CA since they contain a higher degree of stochastic variation leading to a simulation uncertainty. This chapter will explain how the simulation power of CA can be optimized by combining them with the machine learning algorithm support vector machines (SVMs). Conceptually in SVMs, input vectors are projected in a higher-dimensional feature space in which an optimal separating hyperplane can be constructed for separating the input data into two or more classes. In the comparative analysis, the integrated modeling approach is carried out for a unique postindustrial European agglomeration: The Ruhr Area. It will be demonstrated how the AI learning approach is implemented, calibrated, validated and applied for the prediction of the regional urban land-cover pattern between 1975 and 2005. Finally, the probability effects will be visualized with the concept of urban DNA

Differential growth of wrinkled biofilms

Biofilms are antibiotic-resistant bacterial aggregates that grow on moist surfaces and can trigger hospital-acquired infections. They provide a classical example in biology where the dynamics of cellular communities may be observed and studied. Gene expression regulates cell division and differentiation, which affect the biofilm architecture. Mechanical and chemical processes shape the resulting structure. We gain insight into the interplay between cellular and mechanical processes during biofilm development on air-agar interfaces by means of a hybrid model. Cellular behavior is governed by stochastic rules informed by a cascade of concentration fields for nutrients, waste and autoinducers. Cellular differentiation and death alter the structure and the mechanical properties of the biofilm, which is deformed according to Foppl-Von Karman equations informed by cellular processes and the interaction with the substratum. Stiffness gradients due to growth and swelling produce wrinkle branching. We are able to reproduce wrinkled structures often formed by biofilms on air-agar interfaces, as well as spatial distributions of differentiated cells commonly observed with B. subtilis.Comment: 19 pages, 13 figure

Pattern Recognition

A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition

Connectivity of Boolean Satisfiability

For Boolean satisfiability problems, the structure of the solution space is characterized by the solution graph, where the vertices are the solutions, and two solutions are connected iff they differ in exactly one variable. For this implicitly defined graph, we here study the st-connectivity and connectivity problems. Building on the work of Gopalan et al. ("The Connectivity of Boolean Satisfiability: Computational and Structural Dichotomies", 2006/2009), we first investigate satisfiability problems given by CSPs, more exactly CNF(S)-formulas with constants (as considered in Schaefer's famous 1978 dichotomy theorem); we prove a computational dichotomy for the st-connectivity problem, asserting that it is either solvable in polynomial time or PSPACE-complete, and an aligned structural dichotomy, asserting that the maximal diameter of connected components is either linear in the number of variables, or can be exponential; further, we show a trichotomy for the connectivity problem, asserting that it is either in P, coNP-complete, or PSPACE-complete. Next we investigate two important variants: CNF(S)-formulas without constants, and partially quantified formulas; in both cases, we prove analogous dichotomies for st-connectivity and the diameter; for for the connectivity problem, we show a trichotomy in the case of quantified formulas, while in the case of formulas without constants, we identify fragments of a possible trichotomy. Finally, we consider the connectivity issues for B-formulas, which are arbitrarily nested formulas built from some fixed set B of connectives, and for B-circuits, which are Boolean circuits where the gates are from some finite set B; we prove a common dichotomy for both connectivity problems and the diameter; for partially quantified B-formulas, we show an analogous dichotomy.Comment: PhD thesis, 82 pages, contains all results from the previous papers arXiv:1312.4524, arXiv:1312.6679, and arXiv:1403.6165, plus additional findings. arXiv admin note: text overlap with arXiv:cs/0609072 by other author

On Cells and Agents : Geosimulation of Urban Sprawl in Western Germany by Integrating Spatial and Non-Spatial Dynamics

Urban sprawl is one of the most challenging land-use and land-cover changes in Germany implicating numerous consequences for the anthropogenic and geobiophysical spheres. While the population and job growth rates of most urban areas stagnate or even decrease, the morphological growth of cities is ubiquitous. Against this backdrop, the quantitative and qualitative modeling of urban dynamics proves to be of central importance. Geosimulation models like cellular automata (CA) and multi-agent systems (MAS) treat cities as complex urban systems. While CA focus on their spatial dynamics, MAS are well-suited for capturing autonomous individual decision making. Yet both models are complementary in terms of their focus, status change, mobility, and representations. Hence, the coupling of CA and MAS is a useful way of integrating spatial pattern and non-spatial processes into one modeling infrastructure. The thesis at hand aims at a holistic geosimulation of the future urban sprawl in the Ruhr. This region is particularly challenging as it is characterized by two seemingly antagonistic processes: urban growth and urban shrinkage. Accordingly, a hybrid modeling approach is to be developed as a means of integrating the simulation power of CA and MAS. A modified version of SLEUTH (short for Slope, Land-use, Exclusion, Urban, Transport, and Hillshade) will function as the CA component. SLEUTH makes use of historic urban land-use data sets and growth coefficients for the purpose of modeling physical urban expansion. In order to enhance the simulation performance of the CA and to incorporate important driving forces of urban sprawl, SLEUTH is for the first time combined with support vector machines (SVM). The supported CA will be coupled with ReHoSh (Residential Mobility and the Housing Market of Shrinking City Systems). This MAS models population patterns, housing prices, and housing demand in shrinking regions. All dynamics are based on multiple interactions between different household groups as well as stakeholders of the housing market. Moreover, this thesis will elaborate on the most important driving factors, rates, and most probable locations of urban sprawl in the Ruhr as well as on the future migration tendencies of different household types and the price development in the housing market of a polycentric shrinking region. The results of SLEUTH and ReHoSh are loosely coupled for a spatial analysis in which the municipal differences that have emerged during the simulations are disaggregated. Subsequently, a concept is developed in order to integrate the CA and the MAS into one geosimulation approach. The thesis introduces semi-explicit urban weights as a possibility of assessing settlement-pattern dynamics and the regional housing market dynamics at the same time. The model combination of SLEUTH-SVM and ReHoSh is finally calibrated, validated, and implemented for simulating three different scenarios of individual housing preferences and their effects on the future urban pattern in the Ruhr. Applied to a digital petri dish, the generic urban growth elements of the Ruhr are being detected

Considerations in designing a cybernetic simple 'learning' model; and an overview of the problem of modelling learning

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Learning is viewed as a central feature of living systems and must be manifested in any artifact that claims to exhibit general intelligence. The central aims of the thesis are twofold: (1) - To review and critically assess the empirical and theoretical aspects of learning as have been addressed in a multitude of disciplines, with the aim of extracting fundamental features and elements. (2) - To develop a more systematic approach to the cybernetic modelling of learning than has been achieved hitherto. In pursuit of aim (1) above the following discussions are included: Historical and Philosophical backgrounds; Natural learning, both physiological and psychological aspects; Hierarchies of learning identified in the evolutionary, functional and developmental senses; An extensive section on the general problem of modelling of learning and the formal tools, is included as a link between aims (1) and (2). Following this a systematic and historically oriented study of cybernetic and other related approaches to the problem of modelling of learning is presented. This then leads to the development of a state-of-the-art general purpose experimental cybernetic learning model. The programming and use of this model is also fully described, including an elaborate scheme for the manifestation of simple learning

Algorithmic Compositional Methods and their Role in Genesis: A Multi-Functional Real-Time Computer Music System

Algorithmic procedures have been applied in computer music systems to generate compositional products using conventional musical formalism, extensions of such musical formalism and extra-musical disciplines such as mathematical models. This research investigates the applicability of such algorithmic methodologies for real-time musical composition, culminating in Genesis, a multi-functional real-time computer music system written for Mac OS X in the SuperCollider object-oriented programming language, and contained in the accompanying DVD. Through an extensive graphical user interface, Genesis offers musicians the opportunity to explore the application of the sonic features of real-time sound-objects to designated generative processes via different models of interaction such as unsupervised musical composition by Genesis and networked control of external Genesis instances. As a result of the applied interactive, generative and analytical methods, Genesis forms a unique compositional process, with a compositional product that reflects the character of its interactions between the sonic features of real-time sound-objects and its selected algorithmic procedures. Within this thesis, the technologies involved in algorithmic methodologies used for compositional processes, and the concepts that define their constructs are described, with consequent detailing of their selection and application in Genesis, with audio examples of algorithmic compositional methods demonstrated on the accompanying DVD. To demonstrate the real-time compositional abilities of Genesis, free explorations with instrumentalists, along with studio recordings of the compositional processes available in Genesis are presented in audiovisual examples contained in the accompanying DVD. The evaluation of the Genesis system’s capability to form a real-time compositional process, thereby maintaining real-time interaction between the sonic features of real-time sound objects and its selected algorithmic compositional methods, focuses on existing evaluation techniques founded in HCI and the qualitative issues such evaluation methods present. In terms of the compositional products generated by Genesis, the challenges in quantifying and qualifying its compositional outputs are identified, demonstrating the intricacies of assessing generative methods of compositional processes, and their impact on a resulting compositional product. The thesis concludes by considering further advances and applications of Genesis, and inviting further dissemination of the Genesis system and promotion of research into evaluative methods of generative techniques, with the hope that this may provide additional insight into the relative success of products generated by real-time algorithmic compositional processes

Modelling Microstructure-Property Relationships in Polycrystalline Metals using New Fast Fourier Transform-Based Crystal Plasticity Frameworks

The present thesis develops several new full-field, fast Fourier transform (FFT)-based crystal plasticity modelling tools for microstructure engineering. These tools are used to explore elasto-viscoplastic deformation, localized deformation, 3D grain morphology, microstructure evolution, dynamic recrystallization and their effects on formability of polycrystalline metals with particular attention paid to sheet alloys of aluminum and magnesium. The new FFT-based crystal plasticity models developed in this work overcome several inherent problems present in the well-known crystal plasticity finite element method (CP-FEM) and elasto-viscoplastic fast Fourier transform method (EVP-FFT) in solving representative volume element (RVE)-based problems. The new models have demonstrated significant fidelity in simulating various deformation phenomena in polycrystalline metals and prove to be faster and accurate alternatives for obtaining full-field solutions of micromechanical fields in aluminum and magnesium sheet alloys. In particular to the aluminum alloys, which are currently replacing heavier steel parts in the automotive industry, the sheet aluminum alloys have significantly improved corrosion resistance and strength-to-weight properties in comparison to steel. However, aluminum alloys are still outperformed by steel in terms of formability. To improve the formability of an aluminum sheet, one method is to develop physics-based predictive computational tools, which can accurately and efficiently predict the behavior of aluminum alloys and thus allow designing the microstructure with desired properties. Accordingly, in first part of this thesis, a novel numerical framework for modelling large deformation in aluminum alloys is developed. The developed framework incorporates the rate-dependent crystal plasticity theory into the fast Fourier transform (FFT)-based formulation, and this is named as rate tangent crystal plasticity-based fast Fourier transform (i.e., RTCP-FFT) framework. This framework is used as a predictive tool for obtaining stress-strain response and texture evolution in new strain-paths with minimal calibration for aluminum alloys. The RTCP-FFT framework is benchmarked against an existing FFT-based model at small strains and finite element-based model at large strains, respectively, for the case of an artificial Face Centered Cubic (FCC) polycrystal. The predictive capability as well as the computational efficiency of the developed framework are then demonstrated for aluminum alloy (AA) 5754. In the second part of this thesis, the RTCP-FFT framework, developed earlier, is coupled with the Marciniak and Kuczynski (MK) approach to establish a new full-field framework for generating forming limit diagrams (FLDs) of aluminum sheet alloys, e.g., AA3003 and AA5754. The new coupled framework is able to investigate the complex effects of grain morphology, local deformation, local texture and grain interactions on the predictions of forming limit strains. This study reveals that among the various microstructural features, the grain morphology has the strongest effect on the predicted FLDs for aluminum alloys. Furthermore, this study also suggests that the FLD predictions can be significantly improved if the actual grain structure of the material is properly accounted for in the crystal plasticity models. In addition to aluminum alloys, magnesium alloys are getting significant attention by the automotive industry due to their light weight and high specific strength. However, the automotive industry has not been able to take full advantage of the lightweight characteristic of magnesium alloys because of their poor formability at room temperature. Therefore, to enhance the workability and restore their ductility, the magnesium alloys are formed at elevated temperature. High temperature forming of magnesium alloys is often accompanied by dynamic recrystallization (DRX), which allows the final microstructure, as well as the properties of the material (e.g., initial grain size, initial texture, etc.), to be controlled. Therefore, DRX coupled with a full-field crystal plasticity FLD framework can be used as a tool to design microstructure of a material. Since it would be beneficial to be able to redesign the material properties of magnesium alloys using physics-based computational tools than using physical experiments, this work takes a step ahead towards such an outcome by presenting a new framework that predicts DRX and models its effects on the formability of magnesium alloys. Accordingly, in the third part of this thesis, a new full-field, efficient and mesh-free numerical framework, to model microstructure evolution, dynamic recrystallization (DRX) and formability in hexagonal closed-packed (HCP) metals such as magnesium alloys at warm temperatures, is developed. This coupled framework combines three new FFT-based approaches, namely: (a) crystal plasticity modelling of HCP alloys, (b) DRX model, and (c) MK model. First, a rate tangent-fast Fourier transform-based elasto-viscoplastic crystal plasticity constitutive model for HCP metals (RTCP-FFT-HCP) is developed. Then, it is coupled with a probabilistic cellular automata (CA) approach to model DRX. Furthermore, this new model is coupled with the Marciniak-Kuczynski (M-K) approach to model formability of magnesium alloys at elevated temperatures. The RTCP-FFT-HCP model computes macro stress-strain response, twinning volume fraction, micromechanical fields, texture evolution and local dislocation density. Nucleation of new grains and their subsequent growth is modeled using the cellular automata approach with probabilistic state switching rule. This framework is validated at each level of the coupling for magnesium sheet alloy, AZ31. First, the RTCP-FFT-HCP model is validated by comparing the simulated macro stress-strain responses under uniaxial tension and compression with experimental measurements at room temperature. Furthermore, the texture evolution predicted with the new model is compared with experiments. The predictions show a good agreement with experiments with high degree of accuracy. Next, the forming limit diagrams (FLDs) are simulated at 100 C, 200 C and 300 C, respectively, for AZ31 sheet alloy considering the effects of DRX. The predicted FLDs show very good agreement with the experimental measurements. The study reveals that the DRX strongly affects the deformed grain structure, grain size and texture evolution and also highlights the importance accounting for DRX during FLD simulations at high temperatures