Cellular Automata Algorithm Research Articles

A modified average-roulette cellular automaton algorithm for optimization tasks

A modified average-roulette cellular automaton algorithm for optimization tasks

A landslide runout model for sediment transport, landscape evolution, and hazard assessment applications

Abstract. We developed a new rule-based, cellular-automaton algorithm for predicting the hazard extent, sediment transport, and topographic change associated with the runout of a landslide. This algorithm, which we call MassWastingRunout (MWR), is coded in Python and implemented as a component for the package Landlab. MWR combines the functionality of simple runout algorithms used in landscape evolution and watershed sediment yield models with the predictive detail typical of runout models used for landslide inundation hazard mapping. An initial digital elevation model (DEM), a regolith depth map, and the location polygon of the landslide source area are the only inputs required to run MWR to model the entire runout process. Runout relies on the principle of mass conservation and a set of topographic rules and empirical formulas that govern erosion and deposition. For the purpose of facilitating rapid calibration to a site, MWR includes a calibration utility that uses an adaptive Bayesian Markov chain Monte Carlo algorithm to automatically calibrate the model to match observed runout extent, deposition, and erosion. Additionally, the calibration utility produces empirical probability density functions of each calibration parameter that can be used to inform probabilistic implementation of MWR. Here we use a series of synthetic terrains to demonstrate basic model response to topographic convergence and slope, test calibrated model performance relative to several observed landslides, and briefly demonstrate how MWR can be used to develop a probabilistic runout hazard map. A calibrated runout model may allow for region-specific and more insightful predictions of landslide impact on landscape morphology and watershed-scale sediment dynamics and should be further investigated in future modeling studies.

Controllable Fabrication of Gallium Ion Beam on Quartz Nanogrooves.

Nanogrooves with high aspect ratios possess small size effects and high-precision optical control capabilities, as well as high specific surface area and catalytic performance, demonstrating significant application value in the fields of optics, semiconductor processes, and biosensing. However, existing manufacturing methods face issues such as complexity, high costs, low efficiency, and low precision, especially in the difficulty of fabricating nanogrooves with high resolution on the nanoscale. This study proposes a method based on focused ion beam technology and a layer-by-layer etching process, successfully preparing V-shaped and rectangular nanogrooves on a silicon dioxide substrate. Combining with cellular automaton algorithm, the ion sputtering flux and redeposition model was simulated. By converting three-dimensional grooves to discrete rectangular slices through a continuous etching process and utilizing the sputtering and redeposition effects of gallium ion beams, high-aspect-ratio V-shaped grooves with up to 9.6:1 and rectangular grooves with nearly vertical sidewalls were achieved. In addition, the morphology and composition of the V-shaped groove sidewall were analyzed in detail using transmission electron microscopy (TEM) and tomography techniques. The influence of the etching process parameters (ion current, dwell time, scan times, and pixel overlap ratio) on groove size was analyzed, and the optimized process parameters were obtained.

Computer Vision Method for Automatic Detection of Microstructure Defects of Concrete.

The search for structural and microstructural defects using simple human vision is associated with significant errors in determining voids, large pores, and violations of the integrity and compactness of particle packing in the micro- and macrostructure of concrete. Computer vision methods, in particular convolutional neural networks, have proven to be reliable tools for the automatic detection of defects during visual inspection of building structures. The study's objective is to create and compare computer vision algorithms that use convolutional neural networks to identify and analyze damaged sections in concrete samples from different structures. Networks of the following architectures were selected for operation: U-Net, LinkNet, and PSPNet. The analyzed images are photos of concrete samples obtained by laboratory tests to assess the quality in terms of the defection of the integrity and compactness of the structure. During the implementation process, changes in quality metrics such as macro-averaged precision, recall, and F1-score, as well as IoU (Jaccard coefficient) and accuracy, were monitored. The best metrics were demonstrated by the U-Net model, supplemented by the cellular automaton algorithm: precision = 0.91, recall = 0.90, F1 = 0.91, IoU = 0.84, and accuracy = 0.90. The developed segmentation algorithms are universal and show a high quality in highlighting areas of interest under any shooting conditions and different volumes of defective zones, regardless of their localization. The automatization of the process of calculating the damage area and a recommendation in the "critical/uncritical" format can be used to assess the condition of concrete of various types of structures, adjust the formulation, and change the technological parameters of production.

Spatial Prediction of Land Cover Change using Cellular Automata Algorithm in Tropical Coastal Area (Case Study in The Rembang Coastal Area, Central Java)

Spatial Prediction of Land Cover Change using Cellular Automata Algorithm in Tropical Coastal Area (Case Study in The Rembang Coastal Area, Central Java)

Microstructural evolution modelling and low-stress fatigue performance of bimodal-structured Al-Mg-Sc-Zr alloy produced by laser powder bed fusion additive manufacturing

ABSTRACT Coarse – and fine-grained bimodal-structures in a Al-Mg base alloy with rare earth elements of Sc/Zr is produced due to the ultrafast nonequilibrium solidification occurs in laser-induced molten pools during laser powder bed fusion (LPBF) additive manufacturing. A novel high-fidelity cellular automaton (CA) algorithm incorporating numerical calculations of molt-pool temperature fields elucidates the formation and evolution of the bimodal-structure. Subsequent heat treatment induces precipitation of Al3(Sc/Zr) particles within the grains, synergistically enhancing strength and plasticity of the LPBF-processed alloy. The crystal plastic finite element method (CPFEM) is used to reveal the synergistic effect between the strength and plasticity during the material tensile procedure. The bimodal-structure exhibits good fatigue resistance but intriguing anisotropy under low stress cyclic loading. It is proved that differentiated distribution patterns relative to the principal stress direction of the bimodal-structure have a significant influence on its fatigue performance. Numerical evolutionary of the bimodal grain deformation reflects this phenomenon.

Development of a non-uniform cellular automata framework for sizing, topology and layout optimization of truss structures

This article presents a bi-level non-uniform cellular automata (CA) algorithm for the solution of sizing, topology and layout optimization of truss structures. The non-uniform CA was successfully used in a previous study to solve the weight optimization problem of truss structures for topology and sizing (El Bouzouiki, Sedaghati, and Stiharu 2021. Computers & Structures 242: 106394). In this article, an extended version of the non-uniform CA algorithm is proposed, based on the fully stressed design approach and the distribution of strain energy within the structure, to find the optimal position of the cell’s (joint’s) coordinates. The proposed non-uniform CA algorithm can solve the minimum weight optimization problem of truss structures subjected to both stress and displacement constraints. Several benchmark problems are presented to demonstrate the efficiency and accuracy of the proposed methodology.

DESIGN AND BUILD VR HORROR GAMES WITH PROCEDURAL CONTENT GENERATION USING CELLULAR AUTOMATA ALGORITHM

The development of video games is very fast according to data obtained by ESA in 2021, about 226 million people on American played video games and in Indonesia 105 million people play video games and this will continue to increase, it is predicted that in 2025 video players gaming in Indonesia will reach 127 million people, this causes the need for game content continues to increase while for making game content it is not something that cheap, one way to reduce costs in making content in the game is to implement procedural content generation, procedural content generation is one way of creating content game automatically by using an algorithm. This research aims to design and build a virtual reality horror game using use cellular automata algorithm, cellular automata algorithm in research This research is used for map making, besides that this research also aims to measure the level of player satisfaction with the game that has been made. Game created using the Unity 3D game engine and the C# programming language. After game has been successfully built, will measure the level of satisfaction through cakes. questionnaire using GUESS-18, based on tests that have been carried out with GUESS-18, the results obtained are 77.1 percent, which can be concluded that the level of player satisfaction is categorized as good.

Full-period fringe order correction method based on dual-threshold cellular automaton

In traditional temporal phase unwrapping (TPU) methods, the fringe order is obtained pixel-to-pixel by multi-frequencies or multi-wavelengths. The direct calculation and error non-accumulation features accelerate the prosperity of TPU. Nevertheless, the noise, defocusing, and non-uniform reflectivity may inevitably lead to some fringe order errors. These errors are powerless against the evolving TPUs such as Gray code and phase coding. In this paper, a full-period fringe order correction method based on dual-threshold cellular automaton is proposed for machine 3D vision. Initially, an advanced dual-threshold cellular automaton algorithm is employed to extract both the strong and weak edges of the wrapped phase individually. Superior to conventional single-threshold edge detection methods, this approach effectively mitigates the edge breakage problem caused by significant height variations. Subsequently, the N-filled map, N-residue mask, and N-start points concepts are introduced to sequentially segment independent connected domains based on the tightly graphical coupling. In addition, by performing edge compensation in the same period, accurate fringe order correction can be achieved even if excessive edge points are identified, as long as the broken edges are effectively connected. The experimental results exhibit exceptional correction performance of the proposed method for intricate isolated objects, objects with non-uniform reflectivity, and dynamic scenes.

Compaction and sintering effects on scaling law of permeability-porosity relation of powder materials

Analysis and understanding of the permeability-porosity relationship of powder materials by cold compaction and solid-state hot sintering is very important for material design and optimization. This work presents a pore-scale numerical framework, including a discrete element method for powder cold compaction, a cellular automata algorithm with curvature-driving redistribution for solid-state hot sintering and a lattice Boltzmann method for fluid flow simulation, to study the permeability-porosity relationship of materials by cold compaction and solid-state hot sintering processes. The results show that the cubic scaling law always holds for the porous materials by cold compaction, while the hot sintering process decreases the permeability dramatically at low porosity. An exponential decay function has been proposed as a correction factor to the permeability-porosity relationship of materials by compaction & sintering processes, which predicts the permeability asymptotically approaching to the cubic scaling law at high porosity while agreeing well with both numerical and experimental data at low porosity. The microstructure evolution analysis shows that the small pores that connect large pores may vanish by sintering, which causes the remarkable permeability decrease at low porosity. The excellent agreements between the numerical predictions and the experimental measurements suggest that the proposed numerical framework provides a powerful tool for analysis and optimization of material designs.

Cellular automata imbedded memristor-based recirculated logic in-memory computing

Memristor-based circuits offer low hardware costs and in-memory computing, but full-memristive circuit integration for different algorithm remains limited. Cellular automata (CA) has been noticed for its well-known parallel, bio-inspired, computational characteristics. Running CA on conventional chips suffers from low parallelism and high hardware costs. Establishing dedicated hardware for CA remains elusive. We propose a recirculated logic operation scheme (RLOS) using memristive hardware and 2D transistors for CA evolution, significantly reducing hardware complexity. RLOS’s versatility supports multiple CA algorithms on a single circuit, including elementary CA rules and more complex majority classification and edge detection algorithms. Results demonstrate up to a 79-fold reduction in hardware costs compared to FPGA-based approaches. RLOS-based reservoir computing is proposed for edge computing development, boasting the lowest hardware cost (6 components/per cell) among existing implementations. This work advances efficient, low-cost CA hardware and encourages edge computing hardware exploration.

PROGENY: A GRASSHOPPER PLUG-IN THAT AUGMENTS CELLULAR AUTOMATA ALGORITHMS FOR 3D FORM EXPLORATIONS

Cellular automata (CA) is a well-known computation method introduced by John von Neumann and Stanislaw Ulam in the 1940s. Since then, it has been studied in various fields such as computer science, biology, physics, chemistry, and art. The Classic CA algorithm is a calculation of a grid of cells' binary states based on neighboring cells and a set of rules. With the variation of these parameters, the CA algorithm has evolved into alternative versions such as 3D CA, Multiple neighborhood CA, Multiple rules CA, and Stochastic CA (Url-1). As a rule-based generative algorithm, CA has been used as a bottom-up design approach in the architectural design process in the search for form (Frazer,1995; Dinçer et al., 2014), in simulating the displacement of individuals in space, and in revealing complex relations at the urban scale (Güzelci, 2013). There are implementations of CA tools in 3D design software for designers as additional scripts or plug-ins. However, these often have limited ability to create customized CA algorithms by the designer. This study aims to create a customizable framework for 3D CA algorithms to be used in 3D form explorations by designers. Grasshopper3D, which is a visual scripting environment in Rhinoceros 3D, is used to implement the framework. The main difference between this work and the current Grasshopper3D plug-ins for CA simulation is the customizability and the real-time control of the framework. The parameters that allow the CA algorithm to be customized are; the initial state of the 3D grid, neighborhood conditions, cell states and rules. CA algorithms are created for each customizable parameter using the framework. Those algorithms are evaluated based on the ability to generate form. A voxel-based approach is used to generate geometry from the points created by the 3D cellular automata. In future, forms generated using this framework can be used as a form generating tool for digital environments.

A cellular automata ship traffic flow model considering navigation rules in narrowing channel

To present an analysis and application method of ship traffic flow considering navigation rules in narrowing channel, which can make a reasonable judgment for decision makers to improve navigation efficiency and ensure navigation safety. In this paper, the relationship model between narrowing channel and ships is analyzed from the perspective of navigation. Based on the constraint condition of navigation safety domain, the navigation rules are formulated for different positions of channel, and the mutual influence of ships at the narrowed position of channel is analyzed emphatically. The establishment scheme of traffic flow model of narrowing channel with navigation safety distance is put forward by using cellular automata algorithm. According to the measured data, the correctness of the ship traffic flow model of narrowing channel is verified. The experimental results show that the relative error rates are between −2.29% and 10.89%, which indicating that the method can better conform to the actual ship traffic flow characteristics of channel narrowing and has strong application value.

The Impact of Forestry Carbon Sink on Land Use Space Based on FLUS Model

Environmental issues are an important issue facing the world in the 21st century. While China’s economy is developing rapidly, the problem of environmental pollution is becoming more and more serious, especially the problem of carbon emissions. Faced with the severe natural ecological environment, China has proposed a dual-carbon goal, that is, China will achieve carbon peaks by 2030 and carbon neutrality by 2060. In order to improve the ecological environment and complete the dual carbon goals on time, in addition to adjusting the industrial structure and improving the technical level to reduce carbon emissions, forestry carbon sink transactions should also be actively used. Forestry carbon sequestration is one of the few carbon sequestration measures that can be implemented at this stage, but the sustainable development of forestry carbon sequestration requires support from land resources, and reasonable land use planning is the premise to ensure forestry carbon sequestration. This research will use the FLUS model based on the artificial neural network algorithm (ANN) and cellular automata algorithm (CA) to analyze the future spatial changes of land use under forestry carbon sink trading and formulate reasonable land planning for sustainable forestry carbon sink trading. FLUS model is a land use simulation algorithm, which is specially used to study the development prediction of land use under different scenarios. The study found that if the forestry carbon sink transaction was implemented, the forest land area in Shenyang could be increased by 303 km2 and 454,500 tons of CO2 could be absorbed annually. The forest land would take the lead in choosing the northern and eastern hilly areas for expansion.

Dynamic Spatial Model of Settlement Land in Tsunami-Prone Areas, Carita District, Banten

Carita District, Banten was one of the districts affected by the tsunami in the Sunda Strait in 2018. With 2.668 hectares of land cover changing mainly in agriculture and urban areas. The land cover affected by the tsunami in the Carita Area consists of horticulture (24.11 ha), rice fields (2.70 ha) and buildings (4.91 ha). Residential areas, infrastructure and service sectors, especially tourism are also affected. According to the Carita District 2022-2042 RDTR (Detailed Spatial Plan), Carita District is still a tsunami-prone area. The purpose of this research is to provide a residential area forecast with spatial models, patterns, and directions for residential area development in the Carita region over the next 20 years. The method used in this research uses a cellular automata algorithm and a dynamic spatial model that utilizes the results to obtain an analytic hierarchy process score from expertise in the Carita District. Based on the results of the analysis there is an increase in settlements of 697%, some of which expanded in the southern and northern areas of Carita District. The results of the simulations that have been carried out can be input for the Carita District government in the direction of land use development

Forest protection and permanence of reduced emissions

Tropical forests are essential for climate change mitigation. With growing interest over the use of credits from reducing emissions from deforestation and forest degradation (REDD+) and other natural climate solutions within both voluntary and compliance carbon markets, key concerns about the long-term durability of the reductions, or their permanence, arise for countries, corporations, regulators, and policy makers. This paper seeks to analyze the longevity of emissions reductions from different policies to slow down and stop deforestation. To establish conditions of permanence, we conduct numerical analyses using a model based on a cellular automata algorithm that learns from historical deforestation patterns and other spatial features in the Brazilian state of Mato Grosso. First, we simulate increased law enforcement to curb deforestation at a jurisdictional scale from 2025 to 2034, followed by potential policy rollbacks from 2035 to 2050. Second, we consider alternative scenarios to avoid potentially legal deforestation coupled with reforestation. We find spatial and path dependence – a successful policy intervention may permanently change the deforestation trajectory even after potential policy reversals. Hence, permanence depends both on the probability of policy reversals and the risk of emissions overshooting. Our results are important for advancing the understanding around the unsettled debate on the permanence of avoided emissions. Further, this paper argues that as policies to prevent deforestation or reduce emissions otherwise are reversible, permanence should be understood and discussed in a probabilistic and time-dependent framework.

Improving dynamic treatment unit forest planning with cellular automata heuristics

We present a model for conducting dynamic treatment unit (DTU) forest planning using a heuristic cellular automata (CA) approach. The clustering of DTUs is driven by entry costs associated with treatments, thus we directly model the economic incentive to cluster. The model is based on the work presented in the literature but enhanced by adding a third phase to the CA algorithm where DTUs are mapped in high detail. The model allows separate but nearby forest areas to be included in the same DTU and shares the entry cost if they are within a defined distance. The model is applied to a typical long-term forest planning problem for a 1 182 ha landscape in northern Sweden, represented by 4 218 microsegments with an average size of 0.28 ha. The added phase increased the utility by 1.5–32.2%. The model produced consistent solutions—more than half of all microsegments were managed with the same treatment program in 95% of all solutions when multiple solutions were found.

The extended Ising model Hamiltonian for order-disorder phase transition on the clean Si (001) surface

We investigated the order-disorder phase transitions with the four spin interacting Ising model Hamiltonian, which was obtained using first principle calculation results for the Si(100) surface. This is the first proposed effective spin Hamiltonian for dimer configurations. However, the four spin interacting Hamiltonian has not been established to date. All simulations were performed using the cellular automaton algorithm adapted for this Hamiltonian. As a result of the simulations made for three different parameter sets, the order-disorder phase transition temperature on the clean Si(001) surface was obtained on average at 203±4 K, which is consistent with the experimental results.

Smooth Pycnophylactic Interpolation Produced by Density-Equalising Map Projections

A large amount of quantitative geospatial data is collected and aggregated in discrete enumeration units (e.g. countries or states). Smooth pycnophylactic interpolation aims to find a smooth, nonnegative function such that the area integral over each enumeration unit is equal to the aggregated data. Conventionally, smooth pycnophylactic interpolation is achieved by a cellular automaton algorithm that converts a piecewise constant function into an approximately smooth function defined on a grid of coordinates on an equal-area map. An alternative approach, proposed by Tobler in 1976, is to construct a density-equalising map projection in which areas of enumeration units are proportional to the aggregated data. A pycnophylactic interpolation can be obtained from the Jacobian of this projection. Here, we describe a software implementation of this method. Although solutions are not necessarily optimal in terms of predefined quantitative measures of smoothness, our method is computationally efficient and can potentially be used in tandem with other methods to accelerate convergence towards an optimal solution.

Predicting Microscale Land Use/Land Cover Changes Using Cellular Automata Algorithm on the Northwest Coast of Peninsular Malaysia

Predicting Microscale Land Use/Land Cover Changes Using Cellular Automata Algorithm on the Northwest Coast of Peninsular Malaysia