Cellular Automata Principles Research Articles

Two-dimensional network scene design for traffic simulation: An approach using computational Delaunay triangulation

Traffic network design is a pivotal aspect of urban planning, necessitating a harmonious blend of theoretical and practical approaches. In this paper, we propose a novel network design methodology utilizing Delaunay triangulation. We address the limitations of raw Delaunay networks, particularly their triangular configurations, by introducing edge-reduction techniques guided by a newly developed metric, detour redundancy. This metric is instrumental in assessing edge importance, facilitating the transformation of the network to better mirror real-world traffic scenarios. The efficacy and practical application of the designed networks are then evaluated through traffic simulations using the principles of cellular automata. Our findings underscore the potential of this approach in enriching traffic network design and pave the way for future investigations into diverse network characteristics and their implications in urban environments.

An optimized 128-bit cellular automata-based hash function for authentication of data at rest and in transit

The cryptographic hash functions are the most fundamental cryptographic concept. These functions are used as basic building blocks for digital signatures and message authentication. Boolean functions are the core of hash functions. These functions are expected to provide pseudo-randomness as well as input sensitivity. Cellular automata are a form of Boolean function that exhibits strong cryptography properties as well as chaotic behavior. This paper proposes a hash function, designed on the principle of cellular automata. The proposed algorithm is secure and meets the requirements for a successful hashing scheme. The hash function has strong statistical and cryptographic characteristics, according to the findings of the avalanche test and the National Institute of Standards and Technology (NIST) Statistical Test Suite. The modularity of different operations of this algorithm makes it suitable for a high-capacity processing environment to produce efficient performance.

An efficient multilevel security architecture for blockchain-based IoT networks using principles of cellular automata.

The tremendous increase in the use of Internet of Things (IoT) has made an impact worldwide by changing the mode of day-to-day life. Like any other application, IoT based networks also have to be protected since the data produced consist of sensitive information. Existing algorithms for providing security in such networks do not consider all the security objectives. Starting from the sensing of data from IoT environment, the data have to be protected from several types of attacks. Also, the authentication of involved entities, integrity of data, access control and confidentiality are to be achieved. This work proposes a novel security architecture for IoT based distributed applications. The architecture uses the best known lightweight cipher ChaCha20. Principles of cellular automata are applied for random number generation to attain more security and randomness. Double encryption ensures multilevel protection of data during the data uploading and storing phases. Providing encryption based on dynamic session keys guarantees the security of the method. It also ensures secure data sharing, mutual authentication between communicating entities, fast execution, user authentication and message integrity. The IoT device connected to a gateway node has to complete registration phase successfully. Subsequently, each time a data transfer between the device and gateway node takes place, mutual authentication phase is executed. Blockchain network used at the edge level ensures authentication of participating nodes and hence, unintended modification of data is prevented. The proposed architecture proves to be efficient in terms of throughput, execution time and resistance to various security attacks.

An Overland Flood Model for Geographical Information Systems

A variety of flood models and commercial flood simulation software are provided in the literature, with different accuracies and precisions changing from coarse to fine, depending on model structure and detailed descriptions of basin and hydrologic properties. These models generally focus on river processes, taking overland processes as inputs of 1D or 2D hydrodynamic or hydrologic river flow models. Due to the discrete structure of overland flow and unknown-dynamic boundary conditions, such classical approaches are not cable of fast and reliable spatio–temporal estimations for overland flows, and require detailed and well-organized spatial data that cannot be immediately obtained during an emergency. A spatially-distributed Geographical Information Systems (GIS) based flood model is developed in this study to simulate overland floods, using cellular automata principles. GIS raster cells are considered hydrologic homogeneous areas throughout which hydrologic properties remain constant. Hydrodynamic flow principles, conservations of mass, momentum and energy are applied at pixel level to simulate floodwaters. The proposed GIS model is capable of directly manipulating spatio–temporal pixel level data (e.g., topography, precipitation, infiltration, surface roughness etc.) for modeling of rainfall-induced overland floods; therefore, it can provide fast, temporal and spatial flood depth estimations as well as maximum flood depths and times of concentration for all pixels throughout a study area. The model is quite simple and easy to apply via easily creatable GIS input layers, and is thus very convenient for preliminary engineering applications that need quick and fast response. Its main advantage is that it does not need a predefined flood boundary and boundary conditions. This advantage is especially valuable for coastal plains where delineation of a basin is generally too difficult. Floodwaters of Cyclone Nargis/Myanmar were simulated to test the model. Sensitivity analyses were applied to evaluate the effects of the model parameters (i.e., surface roughness and infiltration rates) on simulation results. The study shows that the proposed GIS model can be readily applied for the fast and inexpensive modeling of rainfall caused floods in areas where flood boundaries and boundary conditions cannot be clearly identified.

A Protocol for Simulation Modeling of Ridesourcing Services: Optimisation of Fleet Size in an Urban Environment

Ridesourcing services have emerged as an alternative transit option for commuters in metropolitan areas. Western Australia has exhibited the highest growth of ridesourcing services as compared to the other regions of Australia. Although the ridesourcing services have attracted a considerable number of riders, whether such services meet the demand still remains a major concern. The introduction of a pricing strategy, ‘surge pricing’, in order to attract drivers during peak hours has led to concerns related to congestion and emissions. Therefore, it is required to devise strategies to determine the optimal distribution of vehicles in order to meet the spatio-temporal demand of the ridesourcing services. Therefore, this study would develop a simulation model to determine the optimal allocation of drivers to converge to demand through simulation through the principles of Cellular Automata (CA) Theory in order to minimise the drivers and riders’ waiting time and overall travel distance of drivers. The boundary conditions for the simulation would be updated through the feedbacks of drivers and riders based on the field survey. The model would be validated through the open-sourced historical data provided by the Perth Uber Company.

GPU-accelerated track reconstruction in the ALICE High Level Trigger

ALICE (A Large Heavy Ion Experiment) is one of the four major experiments at the Large Hadron Collider (LHC) at CERN. The High Level Trigger (HLT) is an online compute farm which reconstructs events measured by the ALICE detector in real-time. The most compute-intensive part is the reconstruction of particle trajectories called tracking and the most important detector for tracking is the Time Projection Chamber (TPC). The HLT uses a GPU-accelerated algorithm for TPC tracking that is based on the Cellular Automaton principle and on the Kalman filter. The GPU tracking has been running in 24/7 operation since 2012 in LHC Run 1 and 2. In order to better leverage the potential of the GPUs, and speed up the overall HLT reconstruction, we plan to bring more reconstruction steps (e.g. the tracking for other detectors) onto the GPUs. There are several tasks running so far on the CPU that could benefit from cooperation with the tracking, which is hardly feasible at the moment due to the delay of the PCI Express transfers. Moving more steps onto the GPU, and processing them on the GPU at once, will reduce PCI Express transfers and free up CPU resources. On top of that, modern GPUs and GPU programming APIs provide new features which are not yet exploited by the TPC tracking. We present our new developments for GPU reconstruction, both with a focus on the online reconstruction on GPU for the online offline computing upgrade in ALICE during LHC Run 3, and also taking into account how the current HLT in Run 2 can profit from these improvements.

The Simulation System of Hazardous Chemical Gas Diffusion in Plant Based on Cellular Automata

Generally speaking, the process of production, transportation and storage in petrochemical, pesticide, energy and other chemical industry involves the regulation of hazardous chemicals. If not well managed, it will cause leakage accidents, and even bring great harm to nearby residents and the environment. The accurate prediction of diffusion range in the first time can effectively help us to organize the evacuation of people in affected areas, which is of great significance to reduce casualties and property losses. In this paper, a simulation system of dangerous chemical gas diffusion in plant based on cellular automata is proposed. Firstly, in this paper, we introduce the principle and basic structure of cellular automata. Secondly, this paper gives a brief introduction to the Gaussian plume model, which is an excellent gas diffusion model. Thirdly, the three- dimensional model of cellular automata based on Gaussian plume model is proposed. The distribution of the cellular at present is predicted by the state of neighbor cellular in three-dimensional coordinates. Finally, the experimental results are compared with other algorithms in this paper, and the comparison results show that the improved 3D cellular automata model has better simulation results.

Fuzzy Pheromone Potential Fields for Virtual Pedestrian Simulation

The study of collective movement of pedestrians is crucial in various situations, such as evacuation of buildings, stadiums, or external events like concerts or public events. In such situations and under panic conditions, several incidents and disasters may arise, resulting in loss of human lives. Hence, the study and modeling of the pedestrians behavior are imperative in both normal and panic situations. In a previous work, we developed a microscopic model for pedestrian movement based on the algorithm of Ant Colonies and the principles of cellular automata. We took advantage of a fuzzy model to better reflect the uncertainty and vagueness of the perception of space to pedestrians, especially to represent the desirability or blurred visibility of virtual pedestrians. This paper uses the mechanism of artificial potential fields. Said fields provide virtual pedestrians with better visibility of their surroundings and its various components (goals and obstacles). The predictions provided by the first-order traffic flow theory are confirmed by the results of the simulation. The advantage of this model lies in the combination of benefits provided by the model of ants and artificial potential fields in a fuzzy modeling, to better understand the perceptions of pedestrians.

Online Reconstruction and Calibration with Feedback Loop in the ALICE High Level Trigger

ALICE (A Large Heavy Ion Experiment) is one of the four large scale experiments at the Large Hadron Collider (LHC) at CERN. The High Level Trigger (HLT) is an online computing farm, which reconstructs events recorded by the ALICE detector in real-time. The most compute-intense task is the reconstruction of the particle trajectories. The main tracking devices in ALICE are the Time Projection Chamber (TPC) and the Inner Tracking System (ITS). The HLT uses a fast GPU-accelerated algorithm for the TPC tracking based on the Cellular Automaton principle and the Kalman filter. ALICE employs gaseous subdetectors which are sensitive to environmental conditions such as ambient pressure and temperature and the TPC is one of these. A precise reconstruction of particle trajectories requires the calibration of these detectors. As first topic, we present some recent optimizations to our GPU-based TPC tracking using the new GPU models we employ for the ongoing and upcoming data taking period at LHC. We also show our new approach for fast ITS standalone tracking. As second topic, we present improvements to the HLT for facilitating online reconstruction including a new flat data model and a new data flow chain. The calibration output is fed back to the reconstruction components of the HLT via a feedback loop. We conclude with an analysis of a first online calibration test under real conditions during the Pb-Pb run in November 2015, which was based on these new features.

Multi-Agents Computational Experiment on Twice-Linked Model of Unconventional Emergencies Evolution Mechanism

Based on the cellular automaton principle and multi-agents theory of complex systems, the generation and the evolutionary mechanism of unconventional emergencies have been studied and the twice-linked model of he evolutionary mechanism of unconventional emergencies has been established with calculating experiments being carried out in this study. The conclusion is reached that the internal causes of unconventional incident is the accumulation of internal energy. Evolution process of twice-linked systems is a process that the constantly-accumulated energy in the promoted system is first suddenly released, and then the internal energy in the linkage system is stimulated and also released in a large-scale. These two outbreaks are almost at the same time, and the eruption of the linkage system can be more than twice as powerful as that of promoted system. The linkage effect of unconventional emergencies could be totally prevented only when no transfer possibility or 100% ceasing ability in linkage system or no network space density occur.

Simulation of Impact of Classroom Layout on Evacuation Efficiency Based on Cellular Automata

This paper, based on the principle of Cellular Automata, simulated the emergent evacuation from a floor of lecture building with different layouts inside the classrooms commonly seen in colleges in China. The psychological factors and behaviors of students during the emergent evacuation were simulated with the distance parameter as the basic judgment for evacuation direction based on Cellular Automata in the present paper. Meanwhile, the impacts of four different internal layouts on the evacuation time, average density, average velocity, and average flow rate were investigated. Simulation results show that the difference in classroom layout affects the evacuation efficiency. It also proves that the classroom layout in Room1 is the most reasonable when emergency evacuation is involved.

Module-based complexity formation: periodic patterning in feathers and hairs.

Patterns describe order which emerges from homogeneity. Complex patterns on the integument are striking because of their visibility throughout an organism’s lifespan. Periodic patterning is an effective design because the ensemble of hair or feather follicles (modules) allows the generation of complexity, including regional variations and cyclic regeneration, giving the skin appendages a new lease on life. Spatial patterns include the arrangements of feathers and hairs in specific number, size, and spacing.We explorehowa field of equivalent progenitor cells can generate periodically arranged modules based on genetic information, physical–chemical rules and developmental timing. Reconstitution experiments suggest a competitive equilibrium regulated by activators/inhibitors involving Turing reaction-diffusion. Temporal patterns result from oscillating stem cell activities within each module (microenvironment regulation), reflected as growth (anagen) and resting (telogen) phases during the cycling of feather and hair follicles. Stimulating modules with activators initiates the spread of regenerative hair waves, while global inhibitors outside each module (macroenvironment) prevent this. Different wave patterns can be simulated by cellular automata principles. Hormonal status and seasonal changes can modulate appendage phenotypes, leading to ‘organ metamorphosis’, with multiple ectodermal organ phenotypes generated from the same precursors. We discuss potential novel evolutionary steps using this module-based complexity in several amniote integument organs, exemplified by the spectacular peacock feather pattern. We thus explore the application of the acquired knowledge of patterning in tissue engineering. New hair follicles can be generated after wounding. Hairs and feathers can be reconstituted through self-organization of dissociated progenitor cells.

Non-invasive model-based localization of ventricular ectopic centers from multichannel ECG

Non-invasive localization of premature ventricular beat (PVB) foci is very important for medical treatment of numerous cardiac diseases. In this work a model-based method of reconstruction of ectopic center locations is investigated. Within the scope of this method patient's multichannel ECG is used as a reference for optimization of an electrophysiological cardiac model. This model is based on the cellular automaton principle and utilizes anatomical data of the patient. Optimized are coordinates of the ectopic focus as well as excitation conduction velocity of ventricular myocardium. Initial values for these parameters are obtained by solving the linearized problem of electrocardiography in terms of activation times. Optimization is performed by minimization of discrepancy between the simulated and reference ECGs. The aim of the current work is to estimate the quality of ectopic focus localization delivered by this method. Four sample ectopic beats have been simulated, with their foci located in different regions of the left ventricle. 1% Gaussian noise has been introduced into the resulting ECGs. In this way the measured ECG signals for this investigation have been obtained. Afterwards the origin of each ectopic beat has been reconstructed using the model-based approach. The method has demonstrated reliable localization of PVB foci, reconstruction errors have not exceeded 6.1 mm.

Efficient Multiobjective Storm Sewer Design Using Cellular Automata and Genetic Algorithm Hybrid

Optimal sewer design aims to find cost-effective solutions for designing sewer networks, and genetic algorithms (GAs) are one of the state-of-the-art optimization techniques that have been applied to this problem. However, finding good quality solutions by using a GA can be prohibitively time consuming, especially when designing large networks. This paper introduces an efficient and robust hybrid optimization method, which deals with the design task in a multiobjective optimization manner using two consecutive stages. A localized approach based on cellular automata principles is applied at the first stage to obtain a set of preliminary solutions, which are then used to seed a multiobjective genetic algorithm (MOGA) at the second stage. Two large real sewer networks are tested for case studies. Results clearly show that the hybrid approach can surpass the standard MOGA in terms of optimization efficiency and quality of solutions.

Evolution of constrained layer damping using a cellular automaton algorithm

Constrained layer damping (CLD) is a highly effective passive vibration control strategy if optimized adequately. Factors controlling CLD performance are well documented for the flexural modes of beams but not for more complicated mode shapes or structures. The current paper introduces an approach that is suitable for locating CLD on any type of structure. It follows the cellular automaton (CA) principle and relies on the use of finite element models to describe the vibration properties of the structure. The ability of the algorithm to reach the best solution is demonstrated by applying it to the bending and torsion modes of a plate. Configurations that give the most weight-efficient coverage for each type of mode are first obtained by adapting the existing ‘optimum length’ principle used for treated beams. Next, a CA algorithm is developed, which grows CLD patches one at a time on the surface of the plate according to a simple set of rules. The effectiveness of the algorithm is then assessed by comparing the generated configurations with the known optimum ones.

RBFFCA: A Hybrid Pattern Classifier Using Radial Basis Function and Fuzzy Cellular Automata

A hybrid learning algorithm, termed as RBFFCA, for the solution of classification problems with real valued inputs is proposed. It comprises an integration of the principles of radial basis function (RBF) and fuzzy cellular automata (FCA). The FCA has been evolved through genetic algorithm (GA) formulation to perform pattern classification task. The versatility of the proposed hybrid scheme is illustrated through its application in diverse fields. Simulation results conducted on benchmark database show that the hybrid pattern classifier achieves excellent performance both in terms of classification accuracy and learning efficiency. Extensive experimental results supported with analytical formulation establish the effectiveness of RBFFCA based pattern classifier and prove it as an efficient and cost-effective alternative for the classification problem.

A cellular operator model for the simulation of static recrystallization

This paper introduces an advanced recrystallization model, based on the principles of cellular automata. The model employs a scalable subgrid technique for efficient tracking of local variations during recrystallization and provides excellent statistics of grain size and texture after recrystallization. Grain boundary nucleation, transition band nucleation and particle-stimulated nucleation were incorporated in the model. This model can interface with other microstructural models, such as a deformation texture model, a dislocation density based flow stress model and a precipitation model. The sensitivity of the model with respect to minute changes of microstructure and microchemistry was tested. Finally, a through-process exercise was conducted to assess the performance of the model in multi-step simulations.

Simulation of self-organised dislocation structures in f.c.c. and b.c.c. single crystals

A computer simulation model based on the principle of cellular automata and molecular dynamics is developed for the problem of dislocation structure formation. The presented simulations show the influence of the used material data for f.c.c. and b.c.c. crystals, the cut-off radius used for simulation, the load conditions (tensile, relaxation and cyclic deformation), and the load direction in the glide plane. Also, an approach is presented which is based on non-linear non-equilibrium thermodynamics and self organisation theory. This dissipative approach enables us to state qualitatively and quantitatively the conditions which lead to dislocation structures. The ‘Bauschinger-effect’ is simulated on a microscopic level.

A study of nickel-chromium-aluminium composite powders made by a thermo-diffusion process

The principles of cellular automata are discussed, and the detailed design of an expandable cellular automata machine is presented. CART, in its simplest form, possesses a cellular array size of 250 × 250,or500 × 500 cells, with four planes and a 218 element rule table. The array can be updated at video rates and several common neighbourhood types are directly implemented. The outline of a high-level language for programming cellular automata is given together with a maze solving program. This algorithm solves any maze in a time proportional to the distance between the start and end points of a chosen path.

Pictorial Modeling of Dynamic Systems

The world may be considered to consist of interacting objects. An object-oriented view is missing, however, in traditional system dynamics models. An attempt to develop a simulation methodology based on this view and using extended cellular automata principles is documented in this article. This methodology relies on pictorial entities representing real or abstract objects and a set of intuitive behavior and interaction rules. A simple predator-prey model shows how such a pictorial simulation approach compares with traditional system dynamics models.