Neighborhood detection and rule selection from cellular automata patterns

Using genetic algorithms (GAs) to search for cellular automation (CA) rules from spatio-temporal patterns produced in CA evolution is usually complicated and time-consuming when both, the neighborhood structure and the local rule are searched simultaneously. The complexity of this problem motivates the development of a new search which separates the neighborhood detection from the GA search. In the paper, the neighborhood is determined by independently selecting terms from a large term set on the basis of the contribution each term makes to the next state of the cell to be updated. The GA search is then started with a considerably smaller set of candidate rules pre-defined by the detected neighhorhood. This approach is tested over a large set of one-dimensional (1-D) and two-dimensional (2-D) CA rules. Simulation results illustrate the efficiency of the new algorith

Multi-Scale Modeling of Dynamic Recrystallization in Metals Undergoing Thermo-Mechanical Processing

This study focuses on devising a unified multi-scale numerical framework to predict the grain size evolution by dynamic recrystallization in metals and alloys for an array of severe plastic thermo-mechanical deformation conditions. The model is developed to predict the temporal and spatial grain size evolution of the material subjected to high strain rate and temperature dependent deformation. Dynamic recrystallization evolves by either a continuous grain refinement mechanism around room temperatures or by a discontinuous grain nucleation and growth mechanism at elevated temperatures. The multi-scale model bridges a dislocation density-based constitutive framework with microscale physics-based recrystallization laws to predict both the types of recrystallization phenomena simultaneously. The simulations are conducted within an integrated probabilistic cellular automata-finite element framework to capture the physics of the recrystallization mechanisms. High strain rate loading experiments in conjunction with microstructural characterization tests are conducted for pure copper to characterize the dynamic grain size evolution in the material and evaluated against the model predictions. Synchrotron X-rays are integrated with a modified Kolsky tension bar to conduct in situ temporal characterization of the grain refinement mechanism operating during the dynamic deformation of copper and evaluated against the developed model kinetics. Finally, the model is implemented to predict the grain size evolution developed during the friction stir spot welding of Al 6061-T6 for varying tool rotational speeds. The experiments show that the original microstructure is completely replaced by a recrystallized fine-grained microstructure with the final average grain size and morphology dependent on the process parameters. The model accurately predicts the process temperature rise with increasing tool rotational speeds, which results in a higher rate of grain coarsening during the dynamic recrystallization phenomenon

Aspects of algorithms and dynamics of cellular paradigms

Els paradigmes cel·lulars, com les xarxes neuronals cel·lulars (CNN, en anglès) i els autòmats cel·lulars (CA, en anglès), són una eina excel·lent de càlcul, al ser equivalents a una màquina universal de Turing. La introducció de la màquina universal CNN (CNN-UM, en anglès) ha permès desenvolupar hardware, el nucli computacional del qual funciona segons la filosofia cel·lular; aquest hardware ha trobat aplicació en diversos camps al llarg de la darrera dècada. Malgrat això, encara hi ha moltes preguntes a obertes sobre com definir els algoritmes d'una CNN-UM i com estudiar la dinàmica dels autòmats cel·lulars. En aquesta tesis es tracten els dos problemes: primer, es demostra que es possible acotar l'espai dels algoritmes per a la CNN-UM i explorar-lo gràcies a les tècniques genètiques; i segon, s'expliquen els fonaments de l'estudi dels CA per mitjà de la dinàmica no lineal (segons la definició de Chua) i s'il·lustra com aquesta tècnica ha permès trobar resultats innovadors.Los paradigmas celulares, como las redes neuronales celulares (CNN, eninglés) y los autómatas celulares (CA, en inglés), son una excelenteherramienta de cálculo, al ser equivalentes a una maquina universal deTuring. La introducción de la maquina universal CNN (CNN-UM, eninglés) ha permitido desarrollar hardware cuyo núcleo computacionalfunciona según la filosofía celular; dicho hardware ha encontradoaplicación en varios campos a lo largo de la ultima década. Sinembargo, hay aun muchas preguntas abiertas sobre como definir losalgoritmos de una CNN-UM y como estudiar la dinámica de los autómatascelular. En esta tesis se tratan ambos problemas: primero se demuestraque es posible acotar el espacio de los algoritmos para la CNN-UM yexplorarlo gracias a técnicas genéticas; segundo, se explican losfundamentos del estudio de los CA por medio de la dinámica no lineal(según la definición de Chua) y se ilustra como esta técnica hapermitido encontrar resultados novedosos.Cellular paradigms, like Cellular Neural Networks (CNNs) and Cellular Automata (CA) are an excellent tool to perform computation, since they are equivalent to a Universal Turing machine. The introduction of the Cellular Neural Network - Universal Machine (CNN-UM) allowed us to develop hardware whose computational core works according to the principles of cellular paradigms; such a hardware has found application in a number of fields throughout the last decade. Nevertheless, there are still many open questions about how to define algorithms for a CNN-UM, and how to study the dynamics of Cellular Automata. In this dissertation both problems are tackled: first, we prove that it is possible to bound the space of all algorithms of CNN-UM and explore it through genetic techniques; second, we explain the fundamentals of the nonlinear perspective of CA (according to Chua's definition), and we illustrate how this technique has allowed us to find novel results

Simulating Rainfall, Water Evaporation and Groundwater Flow in Three-Dimensional Satellite Images with Cellular Automata

Remote sensing has been used in numerous environmental simulations with the aim of solving and improving many different kinds of problems, e.g., meteorology applications, soil quality studies, water resource exploration, and environmental protection. Besides, cellular automata have been widely used in the field of remote sensing for simulating natural phenomena over two-dimensional satellite images. However, simulations on Digital Elevation Models (DEM), or three-dimensional (3D) satellite images, are scarce. This paper presents a study of modeling and simulation of the weather phenomena of rainfall, water evaporation and groundwater flow in 3D satellite images through a new algorithm, developed by the authors, named RACA (Rainfall with Cellular Automata). The purpose of RACA is to obtain, from the simulation, numerical and 3D results related to the total cumulative flow and maximum level of water that allow us to make decisions on important issues such as analyzing how climate change will affect the water level in a particular area, estimating the future water supply of a population, establishing future construction projects and urban planning away from locations with high probability of flooding, or preventing the destruction of property and human life from future natural disasters in urban areas with probability of flooding

An Evolutionary Approach to Adaptive Image Analysis for Retrieving and Long-term Monitoring Historical Land Use from Spatiotemporally Heterogeneous Map Sources

Land use changes have become a major contributor to the anthropogenic global change. The ongoing dispersion and concentration of the human species, being at their orders unprecedented, have indisputably altered Earth’s surface and atmosphere. The effects are so salient and irreversible that a new geological epoch, following the interglacial Holocene, has been announced: the Anthropocene. While its onset is by some scholars dated back to the Neolithic revolution, it is commonly referred to the late 18th century. The rapid development since the industrial revolution and its implications gave rise to an increasing awareness of the extensive anthropogenic land change and led to an urgent need for sustainable strategies for land use and land management. By preserving of landscape and settlement patterns at discrete points in time, archival geospatial data sources such as remote sensing imagery and historical geotopographic maps, in particular, could give evidence of the dynamic land use change during this crucial period. In this context, this thesis set out to explore the potentials of retrospective geoinformation for monitoring, communicating, modeling and eventually understanding the complex and gradually evolving processes of land cover and land use change. Currently, large amounts of geospatial data sources such as archival maps are being worldwide made online accessible by libraries and national mapping agencies. Despite their abundance and relevance, the usage of historical land use and land cover information in research is still often hindered by the laborious visual interpretation, limiting the temporal and spatial coverage of studies. Thus, the core of the thesis is dedicated to the computational acquisition of geoinformation from archival map sources by means of digital image analysis. Based on a comprehensive review of literature as well as the data and proposed algorithms, two major challenges for long-term retrospective information acquisition and change detection were identified: first, the diversity of geographical entity representations over space and time, and second, the uncertainty inherent to both the data source itself and its utilization for land change detection. To address the former challenge, image segmentation is considered a global non-linear optimization problem. The segmentation methods and parameters are adjusted using a metaheuristic, evolutionary approach. For preserving adaptability in high level image analysis, a hybrid model- and data-driven strategy, combining a knowledge-based and a neural net classifier, is recommended. To address the second challenge, a probabilistic object- and field-based change detection approach for modeling the positional, thematic, and temporal uncertainty adherent to both data and processing, is developed. Experimental results indicate the suitability of the methodology in support of land change monitoring. In conclusion, potentials of application and directions for further research are given

Un panorama de la télédétection de l'étalement urbain

The objective of this review paper is to provide an overview of remote sensing based research tackling urban sprawl issue. 113 articles were indexed and analyzed after research on bibliographical databases. These 113 articles are presented in the form of summary table giving highlights of the listed publications. Articles are divided into 6 categories (F, A, B, C, D, E) according to whether they are articles of methodology, characterization, prospective modeling-simulation, retrospective modeling-simulation, analysis of impacts or monitoring of urban sprawl. The summary table is conceived as a tool which can help researchers interested by the measurement and the analysis of urban sprawl.Cette note rend compte d'une recherche bibliographique dont l'objectif est de fournir un panorama des recherches utilisant la télédétection pour aborder la problématique de l'étalement urbain. 113 articles ont été répertoriés et analysés à la suite de recherches dans des bases de données bibliographiques. Ces 113 articles sont présentés sous forme de tableau récapitulatif donnant un aperçu général des publications recensées. Les articles sont répartis en 6 catégories (F, A, B, C, D, E) suivant qu'il s'agit d'articles de méthodologie, de caractérisation, de modélisation-simulation prospective, de modélisation-simulation rétrospective, d'analyse d'impacts ou de monitorage de l'étalement urbain. Le panorama est conçu comme un outil d'aide aux chercheurs qui s'intéressent à la mesure et à l'analyse de l'étalement urbain

Novel Approach for Texture-Based Segmentation and classification of Brain Tumors in MR Images

Brain tumor conclusion is a basic endeavor. This structure gives a profitable strategy to the finish of the Brain tumor. The proposed structure involves Texture element extraction from Brain MR images. Classify the brain images on the bases of texture characteristics using ensemble base classifier. After arrangement tumor district is removed from those pictures which are classified as malignant using Fuzzy C-Mean(FCM) gathering using Gabor wavelet features is giving the better-segmented picture. Our proposed framework performed precisely and efficiently. We accomplished exactness and classification within 99.68% and furthermore accomplished the precise after effect of segmentation extricate the tumor area from the brain MR images

Hierarchically Organized Latent Modules for Exploratory Search in Morphogenetic Systems

Self-organization of complex morphological patterns from local interactions is a fascinating phenomenon in many natural and artificial systems. In the artificial world, typical examples of such morphogenetic systems are cellular automata. Yet, their mechanisms are often very hard to grasp and so far scientific discoveries of novel patterns have primarily been relying on manual tuning and ad hoc exploratory search. The problem of automated diversity-driven discovery in these systems was recently introduced [26, 62], highlighting that two key ingredients are autonomous exploration and unsupervised representation learning to describe "relevant" degrees of variations in the patterns. In this paper, we motivate the need for what we call Meta-diversity search, arguing that there is not a unique ground truth interesting diversity as it strongly depends on the final observer and its motives. Using a continuous game-of-life system for experiments, we provide empirical evidences that relying on monolithic architectures for the behavioral embedding design tends to bias the final discoveries (both for hand-defined and unsupervisedly-learned features) which are unlikely to be aligned with the interest of a final end-user. To address these issues, we introduce a novel dynamic and modular architecture that enables unsupervised learning of a hierarchy of diverse representations. Combined with intrinsically motivated goal exploration algorithms, we show that this system forms a discovery assistant that can efficiently adapt its diversity search towards preferences of a user using only a very small amount of user feedback

Evolving Structures in Complex Systems

In this paper we propose an approach for measuring growth of complexity of emerging patterns in complex systems such as cellular automata. We discuss several ways how a metric for measuring the complexity growth can be defined. This includes approaches based on compression algorithms and artificial neural networks. We believe such a metric can be useful for designing systems that could exhibit open-ended evolution, which itself might be a prerequisite for development of general artificial intelligence. We conduct experiments on 1D and 2D grid worlds and demonstrate that using the proposed metric we can automatically construct computational models with emerging properties similar to those found in the Conway's Game of Life, as well as many other emergent phenomena. Interestingly, some of the patterns we observe resemble forms of artificial life. Our metric of structural complexity growth can be applied to a wide range of complex systems, as it is not limited to cellular automata.Comment: IEEE Symposium Series on Computational Intelligence 2019 (IEEE SSCI 2019