Urban growth scenario projection using heuristic cellular automata in arid areas considering the drought impact

Arid areas with low precipitation and sparse vegetation typically yield compact urban pattern,and drought directly impacts urban site selection,growth processes,and future scenarios.Spatial simulation and projection based on cellular automata(CA)models is important to achieve sustainable urban development in arid areas.We developed a new CA model using bat algorithm(BA)named bat algorithm-probability-of-occurrence-cellular automata(BA-POO-CA)model by considering drought constraint to accurately delineate urban growth patterns and project future scenarios of Urumqi City and its surrounding areas,located in Xinjiang Uygur Autonomous Region,China.We calibrated the BA-POO-CA model for the drought-prone study area with 2000 and 2010 data and validated the model with 2010 and 2020 data,and finally projected its urban scenarios in 2030.The results showed that BA-POO-CA model yielded overall accuracy of 97.70%and figure-of-merits(FOMs)of 35.50%in 2010,and 97.70%and 26.70%in 2020,respectively.The inclusion of drought intensity factor improved the performance of BA-POO-CA model in terms of FOMs,with increases of 5.50%in 2010 and 7.90%in 2020 than the model excluding drought intensity factor.This suggested that the urban growth of Urumqi City was affected by drought,and therefore taking drought intensity factor into account would contribute to simulation accuracy.The BA-POO-CA model including drought intensity factor was used to project two possible scenarios(i.e.,business-as-usual(BAU)scenario and ecological scenario)in 2030.In the BAU scenario,the urban growth dominated mainly in urban fringe areas,especially in the northern part of Toutunhe District,Xinshi District,and Midong District.Using exceptional and extreme drought areas as a spatial constraint,the urban growth was mainly concentrated in the"main urban areas-Changji-Hutubi"corridor urban pattern in the ecological scenario.The results of this research can help to adjust urban planning and development policies.Our model is readily applicable to simulating urban growth and future scenarios in global arid areas such as Northwest China and Africa.

Probabilistic Automata-Based Method for Enhancing Performance of Deep Reinforcement Learning Systems

Deep reinforcement learning(DRL) has demonstrated significant potential in industrial manufacturing domains such as workshop scheduling and energy system management.However, due to the model's inherent uncertainty, rigorous validation is requisite for its application in real-world tasks. Specific tests may reveal inadequacies in the performance of pre-trained DRL models, while the “black-box” nature of DRL poses a challenge for testing model behavior. We propose a novel performance improvement framework based on probabilistic automata,which aims to proactively identify and correct critical vulnerabilities of DRL systems, so that the performance of DRL models in real tasks can be improved with minimal model modifications.First, a probabilistic automaton is constructed from the historical trajectory of the DRL system by abstracting the state to generate probabilistic decision-making units(PDMUs), and a reverse breadth-first search(BFS) method is used to identify the key PDMU-action pairs that have the greatest impact on adverse outcomes. This process relies only on the state-action sequence and final result of each trajectory. Then, under the key PDMU, we search for the new action that has the greatest impact on favorable results. Finally, the key PDMU, undesirable action and new action are encapsulated as monitors to guide the DRL system to obtain more favorable results through real-time monitoring and correction mechanisms. Evaluations in two standard reinforcement learning environments and three actual job scheduling scenarios confirmed the effectiveness of the method, providing certain guarantees for the deployment of DRL models in real-world applications.

Application of Cellular Automata and Markov Chain model for urban green infrastructure in Kuala Lumpur,Malaysia

Kuala Lumpur of Malaysia,as a tropical city,has experienced a notable decline in its critical urban green infrastructure(UGI)due to rapid urbanization and haphazard development.The decrease of UGI,especially natural forest and artificial forest,may reduce the diversity of ecosystem services and the ability of Kuala Lumpur to build resilience in the future.This study analyzed land use and land cover(LULC)and UGI changes in Kuala Lumpur based on Landsat satellite images in 1990,2005,and 2021and employed the overall accuracy and Kappa coefficient to assess classification accuracy.LULC was categorized into six main types:natural forest,artificial forest,grassland,water body,bare ground,and built-up area.Satellite images in 1990,2005,and 2021 showed the remarkable overall accuracy values of 91.06%,96.67%,and 98.28%,respectively,along with the significant Kappa coefficient values of 0.8997,0.9626,and 0.9512,respectively.Then,this study utilized Cellular Automata and Markov Chain model to analyze the transition of different LULC types during 1990-2005 and 1990-2021 and predict LULC types in 2050.The results showed that natural forest decreased from 15.22%to 8.20%and artificial forest reduced from 18.51%to 15.16%during 1990-2021.Reductions in natural forest and artificial forest led to alterations in urban surface water dynamics,increasing the risk of urban floods.However,grassland showed a significant increase from 7.80%to 24.30%during 1990-2021.Meanwhile,bare ground increased from 27.16%to 31.56%and built-up area increased from 30.45%to 39.90%during 1990-2005.In 2021,built-up area decreased to 35.10%and bare ground decreased to 13.08%,indicating a consistent dominance of built-up area in the central parts of Kuala Lumpur.This study highlights the importance of integrating past,current,and future LULC changes to improve urban ecosystem services in the city.

基于Aho-Corasick算法的多模式匹配算法研究

通过分析模式匹配算法在网络入侵检测中的重要作用和所面临的问题,对Aho-Corasick基于自动机的算法和以Aho-Corasick算法为基础的相关算法进行了分析,并通过实验对多种多模式匹配算法进行综合对比,最后对多模式匹配方法的发展趋势进行了展望。

Modeling of rock failure based on physical cellular automata

To analyze the effects of heterogeneous material characteristics on rock failure,a micro-heterogeneous physical cellular automata （Mh-PCA） model is introduced according to the cellular automata theory from a general power view.In this model,the neighbor is the Moore pattern and the Weibull distribution is adopted to simulate the rock heterogeneousness.Using this model,the evolvements and acoustic emission of rock failure are simulated for four materials of different degree of homogeneousness （m＝1,5,10,15）.The results show that the heterogeneous characteristic has a great effect on the rock failure,the more the homogeneousness,the fewer the crack branches and the more concentrated acoustic emissions.The physical cellular automata theory gives a new idea for studying rock failure.

Semi-tensor product approach to controllability and stabilizability of finite automata

Using semi-tensor product of matrices, the controllability and stabilizability of finite automata are investigated. By expressing the states, inputs, and outputs in vector forms, the transition and output functions are represented in matrix forms.Based on this algebraic description, a necessary and sufficient condition is proposed for checking whether a state is controllable to another one. By this condition, an algorithm is established to find all the control sequences of an arbitrary length. Moreover, the stabilizability of finite automata is considered, and a necessary and sufficient condition is presented to examine whether some states can be stabilized. Finally, the study of illustrative examples verifies the correctness of the presented results/algorithms.

A cellular automata traffic flow modeling of desired speed variability

The satisfaction rate of desired velocity in the case of a mixture of fast and slow vehicles is studied by using a cellular automaton method. It is found that at low density the satisfaction rate depends on the maximal velocity. However, the behavior of the satisfaction rate as a function of the coefficient of variance is independent of the maximal velocity. This is in good agreement with empirical results obtained by Lipshtat [Phys. Rev. E 79 066110 （2009）]. Furthermore, our numerical result demonstrates that at low density the satisfaction rate takes higher values, whereas the coefficient of variance is close to zero. The coefficient of variance increases with increasing density, while the satisfaction rate decreases to zero. Moreover, we have also shown that, at low density the coefficient variance depends strongly on the probability of overtaking.

Stair evacuation simulation based on cellular automata considering evacuees' walk preferences

As a physical model, the cellular automata （CA） model is widely used in many areas, such as stair evacuation. However, existing CA models do not consider evacuees＇ walk preferences nor psychological status, and the structure of the basic model is unapplicable for the stair structure. This paper is to improve the stair evacuation simulation by addressing these issues, and a new cellular automata model is established. Several evacuees＇ walk preference and how evacuee＇s psychology influences their behaviors are introduced into this model. Evacuees＇ speeds will be influenced by these features. To validate this simulation, two fire drills held in two high-rise buildings are video-recorded. It is found that the simulation results are similar to the fire drill results. The structure of this model is simple, and it is easy to further develop and utilize in different buildings with various kinds of occupants.

Effects of rainy weather on traffic accidents of a freeway using cellular automata model

The aim of this work is to investigate the influence of rainy weather on traffic accidents of a freeway. The micro-scale driving behaviors in rainy weather and possible vehicle rear-end and sideslip accidents are analyzed. An improved CA model of two lanes one-way freeway is presented, where some vehicle accidents will occur when the necessary conditions are simultaneously satisfied. The characteristics of traffic flow under different rainfall intensities are discussed and the accident probabilities are analyzed via the simulation experiments by using variable speed limit （VSL） and incoming flow control. The results indicate that the measures are effective especially during heavy rainstorms or short-time heavy rainfall. According to different rainfall intensities, an appropriate strategy should be adopted in order to reduce the probability of vehicle accidents and enhance traffic flux as well.

Modeling Passengers Boarding in Aircraft Using Cellular Automata

Aircraft are profitable to their owners as long as they are in the air transporting passengers to their destinations;therefore it is vital to minimize as much as possible their preparation time on the ground.In this paper we simulate different boarding strategies with the help of a model based on cellular automata parallel computational tool,attempting to find the most efficient way to deliver each passenger to her/his assigned seat.Two seat arrangements are used,a small one based on Airbus A320/ Boeing 737 and a larger one based on Airbus A380/ Boeing777-300.A wide variety of parameters,including time delay for luggage storing,the frequency by which the passengers enter the plane,different walking speeds of passengers depending on sex,age and height,and the possibility of walking past their seat,are simulated in order to achieve realistic results,as well as monitor their effects on boarding time.The simulation results indicate that the boarding time can be significantly reduced by the simple grouping and prioritizing of passengers.In accordance with previous papers and the examined strategies,the outside-in and reverse pyramid boarding methods outperform all the others for both the small and large airplane seat layout.In the latter,the examined strategies are introduced for first time in an analogous way to the initial small seat arrangement of Airbus A320/ Boeing737 aircraft family.Moreover,since in real world scenarios,the compliance of all the passengers to the suggested group division and boarding strategy cannot be guaranteed,further simulations were conducted.It is clear that as the number of passengers disregarding the priority of the boarding groups increases,the time needed for the boarding to complete tends towards that of the random boarding strategy,thus minimizing the possible advantages gained by the proposed boarding strategies.

Optimizing of large-number-patterns string matching algorithms based on definite-state automata

Because the small CACHE size of computers, the scanning speed of DFA based multi-pattern string-matching algorithms slows down rapidly especially when the number of patterns is very large. For solving such problems, we cut down the scanning time of those algorithms (i.e. DFA based) by rearranging the states table and shrinking the DFA alphabet size. Both the methods can decrease the probability of large-scale random memory accessing and increase the probability of continuously memory accessing. Then the hitting rate of the CACHE is increased and the searching time of on the DFA is reduced. Shrinking the alphabet size of the DFA also reduces the storage complication. The AC++algorithm, by optimizing the Aho-Corasick (i.e. AC) algorithm using such methods, proves the theoretical analysis. And the experimentation results show that the scanning time of AC++and the storage occupied is better than that of AC in most cases and the result is much attractive when the number of patterns is very large. Because DFA is a widely used base algorithm in may string matching algorithms, such as DAWG, SBOM etc., the optimizing method discussed is significant in practice.

Cellular automata model for traffic flow with safe driving conditions

In this paper, a recently introduced cellular automata （CA） model is used for a statistical analysis of the inner micro-scopic structure of synchronized traffic flow. The analysis focuses on the formation and dissolution of clusters or platoons of vehicles, as the mechanism that causes the presence of this synchronized traffic state with a high flow. This platoon formation is one of the most interesting phenomena observed in traffic flows and plays an important role both in manual and automated highway systems （AHS）. Simulation results, obtained from a single-lane system under periodic boundary conditions indicate that in the density region where the synchronized state is observed, most vehicles travel together in pla- toons with approximately the same speed and small spatial distances. The examination of velocity variations and individual vehicle gaps shows that the flow corresponding to the synchronized state is stable, safe and highly correlated. Moreover, results indicate that the observed platoon formation in real traffic is reproduced in simulations by the relation between vehicle headway and velocity that is embedded in the dynamics definition of the CA model.

A cellular automata model of epidemics of a heterogeneous susceptibility

In this paper we present a model with spatial heterogeneity based on cellular automata （CA）. In the model we consider the relevant heterogeneity of host （susceptible） mixing and the natural birth rate. We divide the susceptible population into three groups according to the immunity of each individual based on the classical susceptible-infectedremoved （SIR） epidemic models, and consider the spread of an infectious disease transmitted by direct contact among humans and vectors that have not an incubation period to become infectious. We test the local stability and instability of the disease-free equilibrium by the spectrum radii of Jacobian. The simulation shows that the structure of the nearest neighbour size of the cell （or the degree of the scale-free networks） plays a very important role in the spread properties of infectious disease. The positive equilibrium of the infections versus the neighbour size follows the third power law if an endemic equilibrium point exists. Finally, we analyse the feature of the infection waves for the homogeneity and heterogeneous cases respectively.

Modeling spatio-temporal distribution of soil moisture by deep learning-based cellular automata model

Soil moisture content （SMC） is a key hydrological parameter in agriculture,meteorology and climate change,and understanding of spatio-temporal distributions of SMC in farmlands is important to address the precise irrigation scheduling.However,the hybrid interaction of static and dynamic environmental parameters makes it particularly difficult to accurately and reliably model the distribution of SMC.At present,deep learning wins numerous contests in machine learning and hence deep belief network （DBN） ,a breakthrough in deep learning is trained to extract the transition functions for the simulation of the cell state changes.In this study,we used a novel macroscopic cellular automata （MCA） model by combining DBN to predict the SMC over an irrigated corn field （an area of 22 km^2） in the Zhangye oasis,Northwest China.Static and dynamic environmental variables were prepared with regard to the complex hydrological processes.The widely used neural network,multi-layer perceptron （MLP） ,was utilized for comparison to DBN.The hybrid models （MLP-MCA and DBN-MCA） were calibrated and validated on SMC data within four months,i.e.June to September 2012,which were automatically observed by a wireless sensor network （WSN） .Compared with MLP-MCA,the DBN-MCA model led to a decrease in root mean squared error （RMSE） by 18%.Thus,the differences of prediction errors increased due to the propagating errors of variables,difficulties of knowing soil properties and recording irrigation amount in practice.The sequential Gaussian simulation （s Gs） was performed to assess the uncertainty of soil moisture estimations.Calculated with a threshold of SMC for each grid cell,the local uncertainty of simulated results in the post processing suggested that the probability of SMC less than 25% will be difference in different areas at different time periods.The current results showed that the DBN-MCA model performs better than the MLP-MCA model,and the DBN-MCA model provides a powerful tool for predicting SMC in highly non-linear forms.Moreover,because modeling soil moisture by using environmental variables is gaining increasing popularity,DBN techniques could contribute a lot to enhancing the calibration of MCA-based SMC estimations and hence provide an alternative approach for SMC monitoring in irrigation systems on the basis of canals.

A cellular automata model of traffic flow with variable probability of randomization

Research on the stochastic behavior of traffic flow is important to understand the intrinsic evolution rules of a traffic system. By introducing an interactional potential of vehicles into the randomization step, an improved cellular automata traffic flow model with variable probability of randomization is proposed in this paper. In the proposed model, the driver is affected by the interactional potential of vehicles before him, and his decision-making process is related to the interactional potential. Compared with the traditional cellular automata model, the modeling is more suitable for the driver＇s random decision-making process based on the vehicle and traffic situations in front of him in actual traffic. From the improved model, the fundamental diagram （flow^tensity relationship） is obtained, and the detailed high-density traffic phenomenon is reproduced through numerical simulation.

Minimization of Fuzzy Finite Generalized Automata

Some concepts in Fuzzy Generalized Automata （FGA） are established. Then an important new algorithm which would calculate the minimal FGA is given. The new algorithm is composed of two parts： the first is called E-reduction which contracts equivalent states, and the second is called RE-reduction which removes retrievable states. Finally an example is given to illuminate the algorithm of minimization.

Cellular Automata-based Chloride Ion Diffusion Simulation of Concrete Bridges under Multi-factor Coupling Actions

In order to accurately simulate the diffusion of chloride ion in the existing concrete bridge and acquire the precise chloride ion concentration at given time, a cellular automata （CA）-based model is proposed. The process of chloride ion diffusion is analyzed by the CA-based method and a nonlinear solution of the Fick＇s second law is obtained. Considering the impact of various factors such as stress states, temporal and spatial variability of diffusion parameters and water-cement ratio on the process of chloride ion diffusion, the model of chloride ion diffusion under multi-factor coupling actions is presented. A chloride ion penetrating experiment reported in the literature is used to prove the effectiveness and reasonability of the present method, and a T-type beam is taken as an illustrative example to analyze the process of chloride ion diffusion in practical application. The results indicate that CA-based method can simulate the diffusion of chloride ion in the concrete structures with acceptable precision.

A Two-Lane Cellular Automata Model with Influence of Next-Nearest Neighbor Vehicle

In this paper, we propose a new two-lane cellular automata model in which the influence of the next-nearest neighbor vehicle is considered, The attributes of the traffic system composed of fast-lane and slow-lane are investigated by the new traffic model. The simulation results show that the proposed two-lane traffic model can reproduce some traffic phenomena observed in real traffic, and that maximum flux and critical density are close to the field measurements. Moreover, the initial density distribution of the fast-lane and slow-lane has much influence on the traffic flow states. With the ratio between the densities of slow lane and fast lane increasing the lane changing frequency increases, but maximum flux decreases. Finally, the influence of the sensitivity coefficients is discussed.

Modelling vehicular interactions for heterogeneous traffic flow using cellular automata with position preference

This paper proposes and validates a modified cellular automata model for determining interaction rate （i.e. number of car-following/overtaking instances） using traffic flow data measured in the field. The proposed model considers lateral position preference by each vehicle type and introduces a position preference parameter fl in the model which facilitates gradual drifting towards preferred position on road, even if the gap in front is sufficient. Additionally, the model also improves upon the conven- tional model by calculating safe front and back gap dynamically based on speed and deceleration properties of leader and follower vehicles. Sensitivity analysis was carried out to determine the effect of β on vehicular interac- tions and the model was calibrated and validated using interaction rates observed in the field. Paired tests were conducted to determine the determining interaction rates validity of the model in Results of the simulations show that there is a parabolic relationship between area occupancy and interaction rate of different vehicle types. The model performed satisfactorily as the simulated interaction rate between different vehicle types were found to be statistically similar to those observed in field. Also, as expected, the interaction rate between light motor vehicles （LMVs） and heavy motor vehicles （HMVs） were found to be higher than that between LMVs and three wheelers because LMVs and HMVs share the same lane. This could not be done using conventional CA models as lateral movement rules were dictated by only speeds and gaps. So, in conventional models, the vehicles would end up in positions which are not realistic. The position preference parameter introduced in this model motivates vehicles to stay in their preferred positions. This study demonstrates the use of interaction rate as a measure to validate micro- scopic traffic flow models.

Three-dimensional cellular automata based particle flow simulations of mechanical properties of talus deposit

Based on three-dimensional cellular automata （CA）, a new stochastic simulation model to simulate the microstructures and particle flow of talus deposit is proposed. Ill addition, an auto-modeling program CARS is developed, with which nunaerical simulations can be conducted conveniently. For the problem of simulating mechanical behaviors of talus deposit, spatial anangement or sphere shapes should be considered. In the new modeling method, four sphere anangement models are developed for the particle flow simulation of talus deposit. Numerical results show that the talus deposit has the mechanical characteristics of typical stress-strain curves, as other rock-like materials. The cohesion of talus deposit decreases with increasing rock content, while the internal friction angle increases with increasing rock contents. Finally, numerical simulation is verified with the results of field test.