Automata Model Research Articles

Characterization and simulation of sputtering etched profile by focused gallium ion beam on GaN substrate

With the continuous development of manufacturing technology of micro/nano devices based on GaN, focused gallium ion beam has become a momentous method for machining complex GaN structures. Therefore, this study focuses on the characterization and simulation of sputtering etched profile by Ga FIB on GaN substrate. First, the profile characterization experiments are carried out to analyze the influence of ion dose, dwelling time, and scan area, and the results reflect that the sputtering etched regularity on GaN substrate differs from that on Si substrate, especially in redeposition effect, milling rate, and droplet phenomenon. In addition, the forming mechanism of droplet phenomenon is discussed, and the milling experiments are conducted to research the droplets avoidance method, which shows that the appearance of droplets is impacted by ion dose and dwelling time. Finally, the sputtering yield of GaN under focused gallium ion beam is measured and sorted, and the simulation program is developed based on continuous cellular automaton (CCA) model. This work can provide a guidance for the further application of GaN-based materials.

A model learning based testing approach for spiking neural P systems

This paper proposes a testing approach for spiking neural P systems, significantly different from the past testing research for cell-like P systems. The proposed method provides a solution to the state explosion problem by constructing a series of approximations, using the concept of cover automaton and Angluin-style model learning from queries, more precisely the Ll algorithm for learning a finite cover automaton, adapted to the more general X-machine model. Furthermore, the concept of identifiability, which is an essential prerequisite for the successful application of our method, but also a more general design characteristic inspired from the testing practice, is introduced and investigated in the context of spiking neural P systems. Identifiability of system's components (modules, methods, etc.) is a fundamental criterion used for assessing a system's testability since it allows the components of a system to be identified from the behaviour produced in response to the inputs received and, consequently, maximizes the effectiveness of the testing process.

Post-fire Regeneration Traits of Understorey Shrub Species Modulate Successional Responses to High Severity Fire in Mediterranean Pine Forests

Recurrent fires can impede the spontaneous recruitment capacity of pine forests. Empirical studies have suggested that this can lead to a prolonged replacement of pine forest by shrubland, especially if shrub species are pyrophytic. Model-based studies, however, have suggested that post-fire succession of pine forest under current climatic conditions will eventually tend towards the dominance of oaks under high fire severity and recurrence. These previous modelling studies did not address the role of the various post-fire regeneration traits of the understory shrub species. Considering the dichotomy of obligate seeder vs. resprouter species, either obligate or facultative resprouter, we hypothesized that when the shrubs present are post-fire seeders, the oaks steadily occupy the forest, whereas resprouter shrub species might compete with oaks and delay or arrest post-fire succession. To test this hypothesis, we developed a dynamic, cellular automaton model for simulating post-fire successional transitions in pine forests, including shrubs, pines and oaks, and stochastic fires of regular frequency. Our results showed a strong tendency towards oak dominance as final model state and a very reduced role of fire recurrence in this final state, with low yearly acorn input delaying oak dominance. Most relevantly, and in line with our hypothesis, the trend towards oak dominance depended markedly on the two types of shrub species, being delayed by resprouter species, which extended the shrub-dominated succession stage for several centuries. Our simulation results supported the view that the type of understorey species should be a key consideration in post-fire restoration strategies aiming to enhance fire resilience.

Prediction of the Non-Isothermal Austenitization Kinetics of Fe-C-Cr Low Alloy Steels with Lamellar Pearlite Microstructure.

The austenitization of low alloy steels during rapid heating processes was involved in many kinds of advanced heat treatment technologies. Most of the previous research on the austenitization kinetics was focused on the spherical pearlite microstructures, which were different from the lamellar pearlite microstructures. In the present research, to predict the non-isothermal austenitization process of an Fe-C-Cr steel with lamellar pearlite, a novel 3-dimensional (3D) cellular automata model, which considered the influences of the coupling diffusion of Cr and C, and the interfacial diffusion between pearlite lamellae and the pearlite lamellar orientation, was established based on the thermodynamic equilibrium data obtained from the Thermo-Calc software and the simulation results of the DICTRA module. To clarify the influences of the heating rate on the austenitization kinetics and validate the simulation results, the austenitization processes of a Fe-1C-1.41Cr steel for different heating rates were studied with a series of dilatometric experiments. The good agreements between the cellular automata simulation results and the experimental results showed that the newly proposed cellular automata model is reasonable. The experimental results show an obvious change of the transition activity energies from the low to high heating rates. The transition from partitioning local equilibrium (PLE) to non-partitioning local equilibrium (NPLE) mechanisms was proved with DICTRA simulations. Basing on the simulation results, the influences of the pearlite lamellae orientation on the austenitization kinetics and the topological aspects of austenite grains were evaluated. In addition, the topological aspects of the rapidly austenitized grains were also compared to the normal grains.

Non-uniform Grain Boundary Migration During Static Recrystallization: A Cellular Automaton Study

During static recrystallization, grains often have non-constant and non-uniform growth rates, significantly affecting the recrystallization kinetics and the microstructure after recrystallization. A cellular automaton model was employed in order to evaluate the relative influences of gradients of stored energy, grain boundary curvature, and heterogeneity of grain boundary mobility on the non-uniform migration of grain boundary segments, leading to the formation of protrusions and retrusions. Electron back-scatter diffraction measurements of a cold-rolled copper microstructure served to feed the model. Orientation maps obtained after partial recrystallization were used to assess the model outcome. The model was capable to predict the shapes of recrystallized grains with retrusions and protrusions. Effects of different model assumptions were compared to reveal individual contributions of different factors to grain size distribution, grain shape and boundary roughness. The model predicted a decreasing average grain growth rate as a result of the progressive immobilization of an increasing fraction of grain boundary segments. The model prediction was compared with experimental results, explaining the origin of stationary boundaries and indicating some further improvements necessary to reach quantitative agreement.Graphical

A refined cellular automaton model with dual cruise-control limit for reproducing synchronized traffic flow

In this paper, a cellular automata model with dual cruise-control limit is refined where the length of each cell is set to 1.5 m and each vehicle can still occupy five cells. The prominent feature of this new model is that these vehicles with low-speed and high-speed are not affected by noise and the slow start rule of the Takayasu and Takayasu model is introduced if there is an empty cell in front of the stationary vehicle. Based on the computer simulation results, one obtains that the new refined model can successfully reproduce the empirical characteristics of three-phase traffic flow theory. In the fundamental diagram, there are three-phase traffic states: Free flow, synchronized flow and wide moving jam. In particular, 2[Formula: see text]-characteristic for phase transitions can be presented. Furthermore, the propagation speed of the downstream front of the moving synchronized pattern (MSP) and wide moving jam is within the range of empirical observation data or close to the range of empirical observation data.

Cellular automaton model with the multi-anticipative effect to reproduce the empirical findings of Kerner’s three-phase traffic theory

As the important spatiotemporal state of traffic flow discovered by Kerner’s three-phase traffic theory, the synchronized traffic flow describes a new traffic phase in congested traffic. However, until now most models within the standard traffic theories cannot reproduce it. The average space gap model (ASGM) is a simple cellular automaton model aimed to reproduce various empirical findings discovered by Kerner’s three-phase traffic theory by incorporating the influence of the multi-anticipative effect on vehicle’s deceleration. However, this paper shows that the simulated synchronized flow by ASGM is not consistent with the reality. To this end, the Multi-Anticipative Model (MAM) based on ASGM is proposed, which describes the influence of the multi-anticipative effect on both the accelerations and the decelerations. Simulations indicate that the empirical consistent synchronized flow and related congested patterns can be well reproduced by MAM. Moreover, MAM can reproduce the speed drop in the car following vehicle platoons reported by the empirical observations. Generally, MAM indicates that the multi-anticipative effect can shed light on the understanding and capturing the complex characteristics of traffic flow especially reported by Kerner’s three-phase traffic theory.

Modeling of the Nonuniform Microstructure and Microsegregation Formation of the Shell in Round Billet Continuous Casting

To explore the influence of nonuniform heat transfer on the grain structure and microsegregation of carbon in shells inside the mold, a multiscale model considering heat conduction and dendritic growth is constructed for the round billet continuous casting. Adopting measured heat flux as the boundary conditions, heat conduction in the mold is solved by the finite difference scheme, and dendritic growth is modeled by the cellular automaton model. The temperature distribution of the round billet under measured heat flux is calculated, and the grain structure and the microsegregation of carbon in four selected regions are simulated. Herein, it is shown in the results that the different local heat fluxes lead to dissimilar variations in the surface temperature and grain structure of the shell. A close relationship exists between the carbon microsegregation and local heat flux; the carbon microsegregation decreases with heat flux. The high heat flux accelerates columnar dendritic growth and fines the columnar dendritic array so that the secondary arms are less developed and the microsegregation is improved.

Simulation of α-Al grain formation in high vacuum die-casting Al-Si-Mg alloys with multi-component quantitative cellular automaton method

Al-Si-Mg alloys are the most commonly used material in high vacuum die-casting (HVDC), in which the morphology and distribution of α-Al grains have important effect on mechanical properties. A multi-component quantitative cellular automaton (CA) model was developed to simulate the microstructure and microsegregation of HVDC Al-Si-Mg alloys with different Si contents (7% and 10%) and cooling rates during solidification. The grain number and average grain size with electron backscatter diffraction (EBSD) analysis were used to verify the simulation. The relationship between grain size and nucleation order as well as nuclei density was investigated and discussed. It is found that the growth of grains will be restrained in the location with higher nuclei density. The influence of composition and cooling rate on the solute transport reveals that for AlSi7Mg0.3 alloy the concentration of solute Mg in liquid is higher at the beginning of eutectic solidification. The comparison between simulation and experiment results shows that externally solidified crystals (ESCs) have a significant effect for samples with high cooling rate and narrow solidification interval.

Comprehensive assessment of the water environment carrying capacity based on the spatial system dynamics model, a case study of Yongding River Basin in North China

The Chinese water environment system is severely degraded by intensive anthropogenic activities. To relieve these problems, the national government has proposed to establish a comprehensive evaluation system of water environment carrying capacity (WECC). However, few studies provide a comprehensive depiction of the spatiotemporal heterogeneity of WECC within a basin/region owing to the limitations of interdisciplinary research methods. By integrating the system dynamics model, cellular automaton model, and gridded geographic information system technology, we developed a spatial system dynamics model to explore the spatiotemporal pattern of the WECC of Yongding River Basin in North China. The spatial system dynamics model works well and the simulation accuracy was close to 85%. Our results showed that inadequate water resources and water quality induced the overloaded WECC in Yongding River Basin, which was unevenly distributed within the river basin. From 2017 to 2035, Zhangjiakou often suffered an overload of WECC throughout the year due to quantity-related water shortages. However, in Beijing's mountainous area, the WECC status of control units 1 and 4 was in good status owing to sufficient water quantities. Considering the overload of the WECC and its feedback relationship with anthropogenic activities, effective and feasible management measures, such as reducing agricultural irrigation water use and domestic pollution, improving the rate of sewage collection and treatment, or industrial restructuring should be prioritized in this arid river basin. Our study improved methodologies in WECC modeling for river basins, helped overcome difficulties in revealing the spatiotemporal dynamic patterns of WECC, proposed a calculation method of WECC in terms of population and GDP, and verified that the threshold of WECC was not static, which provides information for finding a balance between economic development and water safety, especially in regions with water scarcity.

Application of Cellular Automata with Improved Dynamic Analysis in Evacuation Management of Sports Events

In this paper, the cellular automaton simulation technology is applied to the evacuation management of sports events, combined with the dynamic analysis method to analyze the collision of people in the evacuation process. At the same time, the cellular automaton model is used to refine the evacuation space to simplify environmental modeling, and the network model is used to determine the individual evacuation path from a macroperspective, simplify modeling, and improve simulation efficiency. In addition, this article simulates the evacuation process of the cellular automaton according to the actual evacuation situation of sports events and constructs the evacuation management system of sports events. Finally, this article evaluates the effect of the model in conjunction with experiments. The experimental results show that the role of the evacuation management system for sports events based on cellular automata proposed in this paper is obvious.

Quotients and atoms of reversible languages

We consider several reversible finite automaton models which have been introduced over decades, and study some properties of their languages. In particular, we look at the question whether the quotients and atoms of a specific class of reversible languages also belong to that class or not. We consider bideterministic automata, reversible deterministic finite automata (REV-DFAs), reversible multiple-entry DFAs (REV-MeDFAs), and several variants of reversible nondeterministic finite automata (REV-NFAs). It is known that the class of REV-DFA languages is strictly included in the class of REV-MeDFA languages. We show that the classes of complete REV-DFA languages and complete REV-MeDFA languages are the same. We also prove that differently from the general case of a REV-DFA language, the minimal DFA for a complete REV-DFA language is a complete REV-DFA. We observe that atoms of any regular language are accepted by REV-NFAs with a single initial and a single final state. We also study atoms of atoms of regular languages and show that any atom of an atom of a regular language is a union of atoms of that language.

Robust decentralized diagnosability of networked discrete event systems against DoS and deception attacks

Denial-of-Service (DoS) are attacks conducted by malicious agents that consists in disrupting, temporally or indefinitely, the services provided by a communication network. When a malicious agent gets access to some network node, it may also perform deception attacks by inserting valid packets with fake information into vulnerable channels. We address, in this paper, DoS and deception attacks (DoS-D attack) that flood some communication channels with fake packets causing delays, loss of observations and insertion of fake observations, and their implications in decentralized fault diagnosability of networked discrete event systems (NDES). To this end, we propose an automaton model for NDES subject to DoS-D attacks that represents the adverse effects of DoS-D attacks on the observations of local diagnosers. We introduce a new codiagnosability definition called DoS-D-robust codiagnosability, and present a necessary and sufficient condition for a language to be DoS-D-robustly codiagnosable. We also propose a verification algorithm for regular languages to check DoS-D-robust codiagnosability.

Forecasting interregional youth migration flows

Subject. This article deals with the issues of youth migration, reproduction processes and the potential for the future socio-economic development of areas. Objectives. The article aims to develop a cellular automata model for the medium-term prediction of inter-regional youth migration flows of the 15–19 age cohort. Methods. For the study, we used a statistical analysis and the cellular automata theory. Results. The article presents a probabilistic cellular automata model with rectangular grids of cells without borders, where the number of cells coincides with the 15–19 age cohort number in the respective area. The article also proposes an author-developed cross-platform program in the Go programming language that helps make medium-term forecasts. Conclusions. The cellular automata allow for integrating various patterns of migratory behavior into a general model of area migration flow, and choosing the most adequate patterns of migratory behavior for various age and social groups of migrants.

Impact of lane function layout on the performance of urban traffic system

In urban traffic systems, alleviating traffic congestion originated from left-turn/U-turn lanes at highly demanded intersections plays an important role in enhancing the traffic efficiency. For a typical intersection, the layout with double left-turn (DLT) lanes is a relatively common geometric feature. However, the effect of DLT scheme on the performance of the entire traffic system is not clear. This paper compares the performance of urban traffic system with different lane function schemes with extended cellular automaton (CA) model considering the car-following, lane-changing, intersection-traversing and route-choice behavior of vehicles. Using the flow-density relation, three macroscopic parameters (average trip completion rate, average traffic flow and average velocity) are examined to evaluate the system performance. With the wide application of DLT scheme, the vehicles can be distributed more evenly among the lanes. As a result, the traffic flow and the trip completion rate will be higher, especially at high density. Case studies of realistic Hefei city network and Sioux Falls networks are also carried out. The results show that the performance of traffic network will be better if more intersections adopt the DLT scheme.

Measuring Dynamics in Evacuation Behaviour with Deep Learning.

Bounded rationality is one crucial component in human behaviours. It plays a key role in the typical collective behaviour of evacuation, in which heterogeneous information can lead to deviations from optimal choices. In this study, we propose a framework of deep learning to extract a key dynamical parameter that drives crowd evacuation behaviour in a cellular automaton (CA) model. On simulation data sets of a replica dynamic CA model, trained deep convolution neural networks (CNNs) can accurately predict dynamics from multiple frames of images. The dynamical parameter could be regarded as a factor describing the optimality of path-choosing decisions in evacuation behaviour. In addition, it should be noted that the performance of this method is robust to incomplete images, in which the information loss caused by cutting images does not hinder the feasibility of the method. Moreover, this framework provides us with a platform to quantitatively measure the optimal strategy in evacuation, and this approach can be extended to other well-designed crowd behaviour experiments.

Development of travel time functions for disrupted urban arterials with microscopic traffic simulation

Urban traffic networks consisting of partially blocked roads often need to remain open to traffic before, during and after disasters because of their vital roles to hazard preparation, emergency response and recovery of urban communities. To conduct effective traffic planning of disrupted transportation networks highly depends on accurate prediction of travel time on partially blocked roads, which is very different from that on intact roads. Due to the lack of appropriate models, travel time prediction approaches developed for intact roads have often been directly applied to partially blocked roads, leading to inaccurate travel time estimates. Unrealistic travel time estimates of partially blocked roads as well as the whole transportation network further affect traffic planning, emergency response and other decision-makings which are heavily reliant on travel time prediction. A new approach to develop travel time functions for partially blocked roads in urban areas is proposed to close this gap based on microscopic traffic simulation. First, an improved model for simulating traffic on partially blocked roads is developed by extending the existing cellular automaton model. Second, the improved traffic model is validated at microscopic and macroscopic levels with measured traffic data from an urban road. Third, traffic simulations under various scenarios with different demand flow rates, truck ratios and blockage ratios are conducted through microscopic simulation experiments. Finally, a set of continuous traffic time functions are further developed for disrupted traffic flow with parameters estimated from the generated traffic data. The developed travel time functions for a typical urban arterial road are then compared with the standard Bureau of Public Roads function. The comparison suggests that the standard Bureau of Public Roads function would considerably underestimate the travel time on partially blocked roads and the proposed travel time functions can offer a more realistic prediction. The proposed methodology of developing the travel time functions of partially blocked roads will be helpful for accurate estimation of traffic demand of post-hazard transportation networks.

Model for tectonic tremors: Enduring events, moment rate spectrum, and moment-duration scaling.

Numerous attempts have been made to obtain earthquake statistics from a theoretical-physics perspective, but these studies mostly involve regular earthquakes. In recent years, a new category of earthquakes, referred to as slow earthquakes, has been discovered. Slow earthquakes emit only weak or no seismic signals and have different statistics than regular earthquakes. Here we propose a physical model for the tremor, which is a type of slow earthquake, introducing two competing timescales in a cellular automaton model. The proposed model reproduces some observation results for tremors, such as enduring events, moment-duration scaling, size distribution, and the power spectrum of the moment rate function.

The Negative Dependence of Evacuation Time on Group Size Under a Binding Mechanism

This paper initiates the analysis of the relation between evacuation time and group size by applying an extended floor-field cellular automaton model. Multi-speed-agents, a group structure containing leaders and followers, and a local-density-dependent dynamic field are implemented all together in the model. Most importantly, a complete binding mechanism which includes leaders waiting for followers is brought up for the first time. A counterintuitive negative relation between evacuation time and group size is discovered in one-group-type simulations, which corresponds to a previous experimental finding. An entropy-like quantity, namely the mixing index, is constructed to analyze the cause of that relation. It is found that under the binding mechanism, the higher degree of group mixing, the longer the evacuation time will be. Moreover, through a constant scale transformation, it is shown that the mixing index can be a key indicator that contains useful information about the evacuation system.

Construction of an Improved English Teaching Model Based on Cellular Automata

In order to improve the effect of college English teaching reform, this paper puts cellular automata English into English teaching simulation and uses the cellular automata model to study the behavior of complex teaching systems. Moreover, this article uses the method of man-machine combination to establish the corresponding dynamic evolution model and design the local evolution rules of the complex system to simulate the English teaching process, including the movement of students and teachers, the interaction of the teaching process, the development of teaching activities, and the processing of teaching resources. In addition, this paper constructs the functional structure of the system and conducts system verification through experimental research. The experimental research results show that the English classroom teaching evaluation system based on the cellular automaton proposed in this article is very effective, and it has a certain role in promoting English teaching.