CA-based Model Research Articles

A framework for real-time operation of urban detention reservoirs: Application of the cellular automata and rainfall nowcasting

Detention reservoirs are employed in urban drainage systems to reduce peak flows downstream of reservoirs. In addition to the volume of detention reservoirs, their operational policies could significantly affect their performance. This paper presents a framework for the real-time coordinated operation of detention reservoirs using deep-learning-based rainfall nowcasting data. Considering the short concentration time of urban basins, the real-time operating policies of urban detention reservoirs should be developed quickly. In the proposed framework, a cellular automata (CA)-based optimization algorithm is linked with the storm water management model (SWMM) to optimize real-time operating policies of gates at the inlets and outlets of detention reservoirs. As CA-based optimization models are not population-based, their computational costs are much less than population-based metaheuristic optimization techniques such as genetic algorithms. To evaluate the applicability and efficiency of the framework, it is applied to the east drainage catchment (EDC) of Tehran metropolitan area in Iran. The results illustrate that the proposed framework could reduce the overflow volume by up to 60%. For complete flood control in the study area, in addition to the real-time operation of detention reservoirs, constructing five tunnels with a total length of 13200 m is recommended. To evaluate the performance of the CA-based optimization model, its results are compared with those obtained from the non-dominated sorting genetic algorithm III (NSGA-III). It is shown that the CA-based model provides similar results with only 5% of the run-time of NSGA-III. A sensitivity analysis is also performed to evaluate the effects of optimization models’ parameters on their performance.

An Improved Mask RCNN Model for Segmentation of ‘Kyoho’ (Vitis labruscana) Grape Bunch and Detection of Its Maturity Level

The ‘Kyoho’ (Vitis labruscana) grape is one of the mainly fresh fruits; it is important to accurately segment the grape bunch and to detect its maturity level for the construction of an intelligent grape orchard. Grapes in the natural environment have different shapes, occlusion, complex backgrounds, and varying illumination; this leads to poor accuracy in grape maturity detection. In this paper, an improved Mask RCNN-based algorithm was proposed by adding attention mechanism modules to establish a grape bunch segmentation and maturity level detection model. The dataset had 656 grape bunches of different backgrounds, acquired from a grape growing environment of natural conditions. This dataset was divided into four groups according to maturity level. In this study, we first compared different grape bunch segmentation and maturity level detection models established with YoloV3, Solov2, Yolact, and Mask RCNN to select the backbone network. By comparing the performances of the different models established with these methods, Mask RCNN was selected as the backbone network. Then, three different attention mechanism modules, including squeeze-and-excitation attention (SE), the convolutional block attention module (CBAM), and coordinate attention (CA), were introduced to the backbone network of the ResNet50/101 in Mask RCNN, respectively. The results showed that the mean average precision (mAP) and mAP0.75 and the average accuracy of the model established with ResNet101 + CA reached 0.934, 0.891, and 0.944, which were 6.1%, 4.4%, and 9.4% higher than the ResNet101-based model, respectively. The error rate of this model was 5.6%, which was less than the ResNet101-based model. In addition, we compared the performances of the models established with MASK RCNN, adding different attention mechanism modules. The results showed that the mAP and mAP0.75 and the accuracy for the Mask RCNN50/101 + CA-based model were higher than those of the Mask RCNN50/101 + SE- and Mask RCNN50/101 + CBAM-based models. Furthermore, the performances of the models constructed with different network layers of ResNet50- and ResNet101-based attention mechanism modules in a combination method were compared. The results showed that the performance of the ResNet101-based combination with CA model was better than the ResNet50-based combination with CA model. The results showed that the proposed model of Mask RCNN ResNet101 + CA was good for capturing the features of a grape bunch. The proposed model has practical significance for the segmentation of grape bunches and the evaluation of the grape maturity level, which contributes to the construction of intelligent vineyards.

Dynamic-wave cellular automata framework for shallow water flow modeling

A new dynamic-wave cellular automata framework for two-dimensional (2D) shallow water flow (SWF) modeling is herein proposed. During the last decade, the concept of cellular automata (CA) has been widely adopted to develop various 2D shallow water flow models. Such CA models use simple transition rules that only consider the conservation of mass and neglect the conservation of momentum, as a result, the models behave like the non-inertia wave approximation which are not suitable for modeling strong discontinuous flows (i.e., transcritical flows and wet-dry interfaces). To overcome this limitation, the present study proposes a new CA framework (SWFCA) for 2D shallow water flow modeling, in which the Bernoulli hydraulic head is adopted to replace water level as the key factor to determine water movements. A novel methodology for linking water depths and velocities in the framework is developed. Model verification is carried out through four cases of regular flows and six benchmark cases with strong discontinuous flows. Model efficiency assessment is also conducted to analyze the numerical performance under various flow conditions. The outcomes indicate that the proposed SWFCA model and a non-inertia wave CA-based model (WCA2D) both provide satisfactory results on regular flows. In modeling strong discontinuous flows, the SWFCA model performs better than the WCA2D model and provides the same accuracy as the finite volume model with a HLLC scheme (FV-HLLC). In the aspect of moving wet-dry interfaces and flows under the partially wet condition, the SWFCA model can achieve higher accuracy than the FV-HLLC model. The numerical efficiency of the SWFCA model relates to the flow conditions involved. As the portion of strong discontinuous flows in the computational domain is increasing, the SWFCA model can be up to 305.5% and 121.0%-1282.2% faster than the WCA2D model and the FV-HLLC model, respectively. This novel CA-based framework has been proved its accuracy and efficiency for shallow water flow modeling. Therefore, it has considerable potentials as a useful tool for real-time flood inundation modeling.

Simulation Performance Evaluation and Uncertainty Analysis on a Coupled Inundation Model Combining SWMM and WCA2D

Urban floods are becoming increasingly more frequent, which has led to tremendous economic losses. The application of inundation modeling to predict and simulate urban flooding is an effective approach for disaster prevention and risk reduction, while also addressing the uncertainty problem in the model is always a challenging task. In this study, a cellular automaton (CA)-based model combining a storm water management model (SWMM) and a weighted cellular automata 2D inundation model was applied and a physical-based model (LISFLOOD-FP) was also coupled with SWMM for comparison. The simulation performance and the uncertainty factors of the coupled model were systematically discussed. The results show that the CA-based model can achieve sufficient accuracy and higher computational efficiency than can a physical-based model. The resolution of terrain and rainstorm data had a strong influence on the performance of the CA-based model, and the simulations would be less creditable when using the input data with a terrain resolution lower than 15 m and a recorded interval of rainfall greater than 30 min. The roughness value and model type showed limited impacts on the change of inundation depth and occurrence of the peak inundation area. Generally, the CA-based coupled model demonstrated laudable applicability and can be recommended for fast simulation of urban flood episodes. This study also can provide references and implications for reducing uncertainty when constructing a CA-based coupled model.

AI-Based Secure Undergraduates’ Ideological and Political Public Opinion Strategy

The popularity of the Internet makes everyone have the right to speak. People are no longer passive receivers of information but propagators of information, and for this reason, cyber opinion comes into existence. Some social events that can arouse people’s sympathy and are related to people’s vital interests will eventually form cyber opinions through netizens’ comments, discussions, and forwarding. As members of the society, undergraduates have different interests, political tendencies, and social cognition. When making judgments and comments on controversial hot issues, undergraduates will make their own correct judgments of authenticity according to their own knowledge and social experience, which will make the propagation content of public opinion more diversified. Especially for ideological and political public opinion, it is very important to ensure the safety of the public opinion propagation model intelligently. In this paper, first, cellular automata (CA) of artificial intelligence (AI) is used to establish the propagation model of undergraduates’ ideological and political safety public opinion and the stochastic immune mechanism model is introduced. Then, a CA-based stochastic immune mechanism model is adopted to simulate the real propagation process of undergraduates’ ideological and political public opinion. Finally, the experimental results demonstrate that the moving probability of public opinion subjects has a great impact on the propagation speed and homogenization rate of cyber public opinion and the proposed CA model can well describe the actual propagation process of cyber public opinion. Meanwhile, the proposed propagation model can ensure the propagation of undergraduates’ ideological and political public opinion on safety.

On the effect of pit shape on pitted plates, Part II: Compressive behavior due to random pitting corrosion

Initiation and growth of pitting corrosion results from electro-chemical reactions closely related to environmental factors, causing great uncertainties in the pit depth, pit size, pit shape and pitting distribution. A cellular automaton (CA) approach was adopted to address the electro-chemical process of pitting corrosion in marine environments, yielding a geometric morphology of corrosion damage. The simulated corrosion pits in the CA-based model, treated as the cylinders, semi-ellipsoids, cones or cavities of hybrid shapes, were introduced into structural analysis models. The effect of pit shape on compressive behavior and ultimate strength of pitted plates was studied, which was linked with the stress interaction between pits. A modelling method based on stochastic simulation was proposed to generate random pitting damage based on limited statistical corrosion data, which could reflect all aspects of the random natures of actual pitting corrosion. Numerical results showed high consistencies with available tests of ultimate strength of pitted plates. The pit shape significantly affects the ultimate strength of pitted structures, likely causing a strength variation more than 20% in a naturally pitted hull plate. This is essentially due to the stress interaction between pits of diverse shapes affecting the onset and development of plasticity in pitted areas.

A Novel Hybrid Approach Based on Cellular Automata and a Digital Elevation Model for Rapid Flood Assessment

An efficient inundation model is necessary for emergency flood responses during storm events. Cellular automata (CA)-based flood models have been proven to produce rapid results while maintaining a certain degree of accuracy. However, the need for computational resources dramatically increases when the number of grid cells increases. Digital elevation model (DEM)-based models generate results even faster, but the simplified governing equations within the models fail to reflect temporal flood evolution. To achieve rapid flood modeling while maintaining model simplicity, a novel two-dimensional hybrid inundation model (HIM) was developed by combining the CA- and DEM-based concepts. Given the temporal flood evolution generated by the CA concept, final finer-scale predictions were obtained by applying the DEM-based concept. The performance of this model was compared to those of widely used, physically based hydraulic models using three UK Environment Agency (EA) benchmark test cases. The HIM yielded consistent prediction results but was faster than the CA-based model. Finally, a comparison was made against flood observations, and the overall root mean squared error (RMSE) for flood depth was 0.388–0.400 m. Considering the uncertainty in the observed flood depths, the HIM shows promising potential to serve as an intermediate tool for emergency response in practical cases.

A cellular automata modelling approach in household water use

Abstract This paper explores the strength of simulation techniques in water conservation by employing a cellular automata (CA) modelling framework. The CA-based model allows the examination of various scenarios with different hypotheses in the context of water resources management in Egypt. Moreover, sensitivity analysis is applied to test the robustness of the model's results. The empirical results show that both social media posts as well as the fear of building the Renaissance Dam in Ethiopia play a motivational role to encourage the individuals to conserve water in Egypt. This is supported by the effort of the most effective and influential categories (family, friends, and relatives, according to the results of the current study) because they are identified as the major influencers on the surrounding people in their social networks. For the results of sensitivity analysis, there is no significant difference between the results of implementing the model and the results of sensitivity analysis.

Modeling spatial distribution of rainfall infiltration amounts in South China using cellular automata and its relationship with the occurrence of collapsing gullies

Collapsing gullies involve considerable erosion and extreme landform changes and frequently occur in South China. The amount of rainfall infiltration has a direct influence on the occurrence of collapsing gullies. However, the mechanistic effect of infiltration or rainfall on the development of collapsing gullies still needs to be explored, as it is difficult to spatially describe infiltration amounts of event rainfall using existing models (e.g., SWAT and WEPP). A model based on cellular automata (CA) was developed for the spatial simulation of infiltration amounts after event rainfall by analyzing water allocation within and between the cells of the model. The spatial distributions of infiltration amounts after event rainfalls with differing intensities and durations were then predicted and spatially compared with the distribution of collapsing gullies. The results demonstrate that the model performs with good accuracy, as indicated by the Nash coefficient of 0.63. The variations among different rainfall infiltration amounts demonstrate that both rainfall intensity and duration promote spatial variability in infiltration amounts and reduce regional infiltration efficiency. The spatial comparison between rainfall infiltration amounts and collapsing gullies shows that rainfall infiltration promotes the occurrence of collapsing gullies, but steep topographical conditions are also required to collapse the hillside soil. The comparison also reveals that a lower-intensity rainfall event contributes more to the development of collapsing gullies than a higher-intensity rainfall event, mainly due to the soil absorbing more rainfall under lower intensity, increasing in soil weight to induce collapse. This CA-based model for spatially simulating infiltration amounts provides an improved and effective method for understanding the spatial and temporal variations in rainfall infiltration and to better capture the trends in the occurrence of collapsing gullies from hydrological effects. The model can also be customized for convenient and practical application in other watersheds.

A cellular automata-based land-use model as an integrated spatial decision support system for urban planning in developing cities: The case of the Bogotá region

Bogotá and the 17 neighboring municipalities make up one of the biggest metropolitan areas in Latin America. However, despite strong functional interactions within the area, there is no official government body at this level in charge of coordinating authorities and providing solutions to the wide variety of issues arising in the regional urban land system. Aiming at providing an insight on future land-use developments linked to new transport infrastructures and at offering a tool to support territorial decision-making, this paper presents a cellular automata-based (CA) model based in Metronamica® software, that allows testing different scenarios based on potential land-use policies, environmental suitability and transport alternatives.There has not been, so far, an urban planning tool that accounts for the complexities of this region. CA-based land-use simulations constitute a useful approach to understanding the impacts of urban planning policies and regulations. This tool can help to improve inter-territorial and inter-institutional coordination, which through planning and management policies seek a spatially integrated development, with a long-term perspective.The CA-based model proposed was calibrated to reproduce land-use changes between 2007 and 2016 using different methods and indicators. The model was used to simulate and analyze eight scenarios with different policy directions of transport infrastructure in the future of the region. The results of the simulations reflect the dynamics of territorial occupation. The calibration indices in the experiment indicate a high degree of suitability for the CA Bogotá model, proving its effectiveness and potential as a useful tool for decision-making. The results show that occupation scenarios with restricted developable zones within the city, tend to have the greater dispersion rate in the study area, compared to scenarios where land development plans are promoted in Bogotá, which representing a more compact development.

A Cellular Automata Model of the Relationship between Adverse Events and Regional Infrastructure Development in an Active War Theater

This study presents a cellular automata (CA) model to assist decision-makers in understanding the effects of infrastructure development projects on adverse events in an active war theater. The adverse events are caused by terrorist activities that primarily target the civilian population in countries such as Afghanistan. In the CA-based model, cells in the same neighborhood synchronously interact with one another to determine their next states, and small changes in iteration yield to complex formations of adverse event risks. The results demonstrate that the proposed model can help in the evaluation of infrastructure development projects in relation to changes in the reported adverse events, as well as in the identification of the geographical locations, times, and impacts of such developments. The results also show that infrastructure development projects have different impacts on the reported adverse events. The CA modeling approach can be used to support decision-makers in allocating infrastructure development funds to stabilize active war regions with higher adverse event risks. Such models can also improve the understanding of the complex interactions between infrastructure development projects and adverse events.

Teaching L2 interactional competence: problems and possibilities

This contribution outlines the problems and possibilities of three issues with regard to the teaching of L2 interactional competence (IC): (1) specifying IC, (2) standardising IC, and (3) translating conversation analytic (CA) insights into classroom practices. In particular, I argue for a shift of discussion from the conceptually treacherous notion of interactional competence to the CA-based model of interactional practices, call attention to the partial, and yet useful, nature of our understanding of such practices as pedagogical objects and of the developmental trajectories towards their mastery, and finally, make evident the daunting obstacles to implementing these understandings in the classroom, while offering illustrative and exemplary exhibits.

Application of cellular automata and Lattice Boltzmann methods for modelling of additive layer manufacturing

PurposeThe holistic numerical model based on cellular automata (CA) and lattice Boltzmann method (LBM) are being developed as part of an integrated modelling approach applied to study the interaction of different physical mechanisms in laser-assisted additive layer manufacturing (ALM) of orthopaedic implants. Several physical events occurring in sequence or simultaneously are considered in the holistic model. They include a powder bed deposition, laser energy absorption and heating of the powder bed by the moving laser beam, leading to powder melting or sintering, fluid flow in the melted pool and flow through partly or not melted material, and solidification. The purpose of this study is to develop a structure of the holistic numerical model based on CA and LBM applicable for studying the interaction of the different physical mechanisms in ALM of orthopaedic implants. The model supposed to be compatible with the earlier developed CA-based model for the generation of the powder bed.Design/methodology/approachThe mentioned physical events are accompanied by heat transfer in solid and liquid phases including interface heat transfer at the boundaries. The sintering/melting model is being developed using LBM as an independent numerical method for hydrodynamic simulations originated from lattice gas cellular automata. It is going to be coupled with the CA-based model of powder bed generation.FindingsThe entire laser-assisted ALM process has been analysed and divided on several stages considering the relevant physical phenomena. The entire holistic model consisting of four interrelated submodels has currently been developed to a different extent. The submodels include the CA-based model of powder bed generation, the LBM-CA-based model of heat exchange and transfer, the thermal solid-liquid interface model and the mechanical solid-liquid interface model for continuous liquid flow.Practical implicationsThe results obtained can be used to explain the interaction of the different physical mechanisms in ALM, which is an intensively developing field of advanced manufacturing of metal, non-metal and composite structural parts, for instance, in bio-engineering. The proposed holistic model is considered to be a part of the integrated modelling approach being developed as a numerical tool for investigation of the co-operative relationships between multiphysical phenomena occurring in sequence or simultaneously during heating of the powder bed by the moving high energy heat source, leading to selective powder sintering or melting, fluid flow in the melted pool and through partly (or not) melted material, as well as solidification. The model is compatible with the earlier developed CA-based model for the generation of the powder bed, allowing for decrease in the numerical noise.Originality/valueThe present results are original and new for the study of the complex relationships between multiphysical phenomena occurring during ALM process based on selective laser sintering or melting, including fluid flow and heat transfer, identified as crucial for obtaining the desirable properties.

A CA-based land system change model: LANDSCAPE

ABSTRACTCellular automata (CA) models are widely used to simulate land-use changes because of their simplicity, flexibility, intuitiveness and ability to incorporate the spatial and temporal dimensions of processes. A small number of CA-based models have been developed to simulate changes in multiple land uses, most of which use the hierarchical allocation strategy and/or inertia factors to enable these CA models to do so accurately. However, only some of these models allow explicit determination of the allocation sequence for active land uses according to the hierarchical allocation strategy and the objective calculation of inertia factors. In this paper, we proposed a CA-based model, i.e. the LAND System Cellular Automata model for Potential Effects (LANDSCAPE), with a hierarchical allocation strategy and resistances, to simulate changes in multiple land uses. Furthermore, we introduced effective ways to objectively determine the allocation sequence for active land uses and calculate resistances for individual land uses. The results show that the LANDSCAPE model, with a calibrated allocation sequence and resistances, is reliable and accurate for simulating multiple land-use changes.

Integrating the multi-label land-use concept and cellular automata with the artificial neural network-based Land Transformation Model: an integrated ML-CA-LTM modeling framework

Cellular automata (CA) and artificial neural networks (ANNs) have been used by researchers over the last three decades to simulate land-use change (LUC). While conventional CA and ANN models assign a cell to only one land-use class, in reality, a cell may belong to several land-use classes simultaneously. The recently developed multi-label (ML) concept overcomes this limitation in land change science. Although the ML concept is a new paradigm with nonexclusive classes and has shown considerable merit in several applications, few studies in land change science have applied it. In addition, determining transition rules in conventional CA is difficult when the number of drivers is large. Since CA has been shown as a potential model to consider neighborhood effects and ANN has been shown effective in determining CA transition rules, we integrated both CA with an ANN model to overcome limitations of each tool. In this study, we specifically extended the ANN-based Land Transformation Model (LTM) with both a CA-based model and the ML concept to create an integrated ML-CA-LTM modeling framework. We also compared, using standard evaluation measures, differences between the proposed integrated model with a conventional CA-based LTM model (called the ml-CA-LTM). Parameterization was made using a learning and testing procedure common in machine learning. Results showed that the modified LUC model, ML-CA-LTM, produced consistently better goodness of fit calibration values compared to the ml-CA-LTM. The outcome of this modified model can be used by managers and decision makers for improved urban planning.

Application of a CA-based model to predict the fire front in Hyrcanian forests of Iran

Forest fire is one of the most important source of land degradation that lead to deforestation and desertification processes. Thus, prediction of forest fire front is necessary to control it. In this study, Alexandridis model based on Cellular Automata (CA) rules was applied to predict the fire front in a part of Hyrcanian forests of Iran. The data of effective factors on fire front in the model (including vegetation type and density, wind speed and direction, and ground elevation) were provided from Mazandaran Natural Resources Administration (MNRA), Mazandaran Meteorological Administration (MMA), and Digital Elevation Model (DEM) of ASTER sensor. The model was used to simulate the front of a wildfire that burned a part of District Three of Neka-Zalemroud forests (DTNZ) on December of 2010. The required data of actual fire for simulation of fire front (including actual fire map, fire area, fire start point, etc.) were provided from MNRA. All effective factors maps for actual fire confine were organized in a Geographic Information System (GIS). The simulation environment was provided based on the ASCII files of altitude, vegetation density, and vegetation type matrices, together with a matrix containing the burned area. The fire front model was programmed and it was implemented by uploading of all digital layers (coding ASCII matrices) of effective variables and considering of the certain wind speed and direction in fire confine. Fire front simulation was run by considering of fire start point coordination and the fire front simulation was depicted. Finally, the number of burned and unburned cells in fire confine matrix was obtained. Results of model implementation including fire front direction and shape were compared with the actual fire confine to evaluate the accuracy of the used model qualitatively. Thus, the fire front polygon was overlaid on the actual fire polygon and the high similarity was observed between them. In addition, total accuracy and Kappa index were used to evaluate the accuracy of the used model quantitatively. The total accuracy and Kappa index were obtained 0.88 and 0.74, respectively. These results can show the accuracy of CA-based model to predict the fire front in Hyrcanian forests of Iran in current research.

Modeling Cache Memory Utilization on Multicore Using Common Pool Resource Game on Cellular Automata

Recent computing architectures are implemented by shared memory technologies to alleviate the high latency experienced by off-chip memory transfers, but the high architectural complexity of modern multicore processors has presented many questions. To tackle the design of efficient algorithms scheduling workloads over available cores, this article presents a parallel bioinspired model that simulates the utilization of shared memory on multicore systems. The proposed model is based on cellular automata (CA) and coupled with game theory principles. CA are selected due to their inherent parallelism and especially their ability to incorporate inhomogeneities. Furthermore, the novelty of the model is realized on the fact that multilevel CA are used to simulate the different levels of cache memory usually found in multicore processors. These characteristics make the model able to cope with the increasing diversity of cache memory hierarchies on modern and future processors. Nonetheless, by acquiring data from hardware performance counters and processing them with the proposed model online, the performance of the system can be calculated and a better scheduling strategy can be adopted in real time. The CA-based model was verified on the behavior of a real multicore system running a multithreaded application, and it successfully simulated the acceleration achieved by an increased number of cores available for the execution of the workload. More specifically, the example of common pool resource from game theory was used with two variations: a static and a variable initial endowment. The static variation of the model approximates slightly better the acceleration of a workload when the number of available processor cores increases, whereas the dynamic variation simulates better the moderate differences due to operation system’s scheduler alternations on the same amount of cores.

Partial validation of cellular automata based model simulations of urban growth: An approach to assessing factor influence using spatial methods

Cellular Automata (CA) based models have a high aptitude to reproduce the characteristics of urban processes and are useful to explore future scenarios. However, validation of their results poses a major challenge due to the absence of real future data with which to compare them. A partial validation applied to a CA-based model for the Madrid Region (Spain) is presented as a proposal for determining the influence of given factors on the results and testing their spatial variability. Several simulations of the model were computed by different combinations of factors, and results were compared using flexible map comparison methods in order to study spatial pattern matches and similarities between them. Main and total effects of these factors were calculated for each method, by applying a simplified Global Sensitivity Analysis approach. Frequency maps showing the most frequent cells with changed land use in the results were generated.

Evolving Transport Networks With Cellular Automata Models Inspired by Slime Mould.

Man-made transport networks and their design are closely related to the shortest path problem and considered amongst the most debated problems of computational intelligence. Apart from using conventional or bio-inspired computer algorithms, many researchers tried to solve this kind of problem using biological computing substrates, gas-discharge solvers, prototypes of a mobile droplet, and hot ice computers. In this aspect, another example of biological computer is the plasmodium of acellular slime mould Physarum polycephalum (P. polycephalum), which is a large single cell visible by an unaided eye and has been proven as a reliable living substrate for implementing biological computing devices for computational geometry, graph-theoretical problems, and optimization and imitation of transport networks. Although P. polycephalum is easy to experiment with, computing devices built with the living slime mould are extremely slow; it takes slime mould days to execute a computation. Consequently, mapping key computing mechanisms of the slime mould onto silicon would allow us to produce efficient bio-inspired computing devices to tackle with hard to solve computational intelligence problems like the aforementioned. Toward this direction, a cellular automaton (CA)-based, Physarum-inspired, network designing model is proposed. This novel CA-based model is inspired by the propagating strategy, the formation of tubular networks, and the computing abilities of the plasmodium of P. polycephalum. The results delivered by the CA model demonstrate a good match with several previously published results of experimental laboratory studies on imitation of man-made transport networks with P. polycephalum. Consequently, the proposed CA model can be used as a virtual, easy-to-access, and biomimicking laboratory emulator that will economize large time periods needed for biological experiments while producing networks almost identical to the tubular networks of the real-slime mould.

Modeling and Simulation on Unidirectional Pedestrian Flow Based on Cellular Automata

The CA-based Model of unidirectional pedestrian flow simulation is established. Model takes into account pedestrian forward, change lanes , side forward, backward , away from the dense crowd behavior. Simulation research and analyzed the relationship between pedestrian flow speed ,flow speed, flow , without moving the proportion of pedestrians and the pedestrian density in different pedestrian density. The results showed that: unidirectional pedestrian flow with the pedestrian density increases, there will be two thresholds K1 and K2. Pedestrian flow velocity will appear slight and obvious decrease after these two thresholds. Pedestrian flow traffic reaches its maximum value at K2. The proportion of unmoving pedestrians will increase and the increased amplitude increases when it reaches at the two thresholds K1 and K2.