The performance of a material is directly affected by its microstructural development during the solidification phase. Discrete cellular automaton (CA) models are widelyused in materials science to simulate and predic...The performance of a material is directly affected by its microstructural development during the solidification phase. Discrete cellular automaton (CA) models are widelyused in materials science to simulate and predict microstructural growth. This review comprehensively explains the developments and applications of CA in solidification structure simulation, including the theoretical underpinnings, computational procedures, software development, and recent advances. Summarizes the potential and limitations of cellular automata in understanding microstructure evolution during solidification, explores the evolution of microstructures during solidification, and adds to our existing knowledge of cellular automaton theory. Finally, the research trend in simulating the evolution of the solidification microstructure using cellular automaton theory is explored.展开更多
The simultaneous increase in development in Pesawaran Regency is closely correlated with the intense competi-tion for land use.However,low policy implementation effectiveness has led to construction beyond designated ...The simultaneous increase in development in Pesawaran Regency is closely correlated with the intense competi-tion for land use.However,low policy implementation effectiveness has led to construction beyond designated spatial plan.The study used a quantitative survey using Landsat images in 2016,2019,and 2022.The data analysis techniques used geographic information systems integrated with Artificial Neural Network(ANN)and Cellular Automata(CA)models.This study aims to predict land-use change in 2031,evaluate its alignment with spatial planning,and provide guidance for controlling land-use change.The results showed that there has been an increase in land use.In 2019,built-up land reached 7,069.65 Ha.The model shows its ability to predict land simulation and transformation,where it is predicted that built-up land in 2031 will experience an increase of up to 40.10%,so development and change cannot be avoided every year.This study also suggests that decision-makers and local governments should reconsider spatial planning strategies.This study shows that there have been many land use changes from 2016 to 2022.The model shows its ability to predict simulation and land transformation.When using the model,there are many changes in the land use area in 2031.This is due to wet agricultural land turning into built-up land by almost 70%.This study shows that road network influence land-use change.The cellular automata model managed to capture the complexity with simple rules.Predictions for future research should focus on conserving wetlands and primary forests.展开更多
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 b...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.展开更多
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...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.展开更多
The development of the Internet of Things(IoT)technology is leading to a new era of smart applications such as smart transportation,buildings,and smart homes.Moreover,these applications act as the building blocks of I...The development of the Internet of Things(IoT)technology is leading to a new era of smart applications such as smart transportation,buildings,and smart homes.Moreover,these applications act as the building blocks of IoT-enabled smart cities.The high volume and high velocity of data generated by various smart city applications are sent to flexible and efficient cloud computing resources for processing.However,there is a high computation latency due to the presence of a remote cloud server.Edge computing,which brings the computation close to the data source is introduced to overcome this problem.In an IoT-enabled smart city environment,one of the main concerns is to consume the least amount of energy while executing tasks that satisfy the delay constraint.An efficient resource allocation at the edge is helpful to address this issue.In this paper,an energy and delay minimization problem in a smart city environment is formulated as a bi-objective edge resource allocation problem.First,we presented a three-layer network architecture for IoT-enabled smart cities.Then,we designed a learning automata-based edge resource allocation approach considering the three-layer network architecture to solve the said bi-objective minimization problem.Learning Automata(LA)is a reinforcement-based adaptive decision-maker that helps to find the best task and edge resource mapping.An extensive set of simulations is performed to demonstrate the applicability and effectiveness of the LA-based approach in the IoT-enabled smart city environment.展开更多
[Objective]Urban floods are occurring more frequently because of global climate change and urbanization.Accordingly,urban rainstorm and flood forecasting has become a priority in urban hydrology research.However,two-d...[Objective]Urban floods are occurring more frequently because of global climate change and urbanization.Accordingly,urban rainstorm and flood forecasting has become a priority in urban hydrology research.However,two-dimensional hydrodynamic models execute calculations slowly,hindering the rapid simulation and forecasting of urban floods.To overcome this limitation and accelerate the speed and improve the accuracy of urban flood simulations and forecasting,numerical simulations and deep learning were combined to develop a more effective urban flood forecasting method.[Methods]Specifically,a cellular automata model was used to simulate the urban flood process and address the need to include a large number of datasets in the deep learning process.Meanwhile,to shorten the time required for urban flood forecasting,a convolutional neural network model was used to establish the mapping relationship between rainfall and inundation depth.[Results]The results show that the relative error of forecasting the maximum inundation depth in flood-prone locations is less than 10%,and the Nash efficiency coefficient of forecasting inundation depth series in flood-prone locations is greater than 0.75.[Conclusion]The result demonstrated that the proposed method could execute highly accurate simulations and quickly produce forecasts,illustrating its superiority as an urban flood forecasting technique.展开更多
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 f...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.展开更多
In this paper, we use some programing tools and algorithms for solving system of word equation for regular languages. There are many possibilities for presentation of regular languages such as grammars, finite automat...In this paper, we use some programing tools and algorithms for solving system of word equation for regular languages. There are many possibilities for presentation of regular languages such as grammars, finite automata, rewriting systems and so on. Some of these systems is presented by system of computational discrete algebra GAP and the possibilities of presentation now in some systems interactive theorem provers (Isabelle, Coq). This computer system can give to detailed understanding of solution of system of word equation, compared the languages and regular expressions of the languages.展开更多
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-r...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.展开更多
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...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.展开更多
Wireless Multimedia Sensor Network (WMSN) is an advancement of Wireless Sensor Network (WSN) that encapsulates WSN with multimedia information like image and video. The primary factors considered in the design and dep...Wireless Multimedia Sensor Network (WMSN) is an advancement of Wireless Sensor Network (WSN) that encapsulates WSN with multimedia information like image and video. The primary factors considered in the design and deployment of WSN are low power consumption, high speed and memory requirements. Security is indeed a major concern, in any communication system. Consequently, design of compact and high speed WMSN with cryptography algorithm for security, without compromising on sensor node performance is a challenge and this paper proposes a new lightweight symmetric key encryption algorithm based on 1 D cellular automata theory. Simulations are performed using MatLab and synthesized using Xilinx ISE. The proposed approach supports both software and hardware implementation and provides better performance compared to other existing algorithms in terms of number of slices, throughput and other hardware utilization.展开更多
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 ar...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.展开更多
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 ir...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.展开更多
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 maxima...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.展开更多
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 anal...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.展开更多
A modified cellular automata （CA） model of dynamic recrystallization （DRX） and a flow stress-based nucleation parameter identification method have been developed. In the method, the modified CA model, which takes ...A modified cellular automata （CA） model of dynamic recrystallization （DRX） and a flow stress-based nucleation parameter identification method have been developed. In the method, the modified CA model, which takes the role of deformation degree on nucleation behavior into consideration, is coupled with an adaptive response surface model （ARSM） to search for the optimum nucleation parameter. The DRX behavior of an oxygen free high conductivity （OFHC） copper with different initial grain sizes has been taken as an example to validate the model. Good agreement is found between the simulated and the experimental results, which demonstrates that the new method can effectively improve the simulation accuracy.展开更多
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...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.展开更多
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 p...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.展开更多
This paper presents a development o f the extended Cellular Automata9CA),based on relational databases(RDB),to model dynamic interactions amon g spatial objects.The integration o f Geographical Information System(GIS)...This paper presents a development o f the extended Cellular Automata9CA),based on relational databases(RDB),to model dynamic interactions amon g spatial objects.The integration o f Geographical Information System(GIS)and CA has the great advantage of simu lationg geographical processes.But standard CA has some restrictions i n cellular shape and neighbourhood and neighbour rules,which restrict the CA’s ability to simulate complex,real world environ-ments.This paper discusses a cell’s spatialrelationbasedonthe spatialobject’s geometricalandmon-geometricalc haracter-istics,and extends the cell’s neighbour definition,and considers that the cell’s neighbour lies in the forms of not on ly spa-tial adjacency but also attribute co rrelation.This paper then puts forw ard that spatial relations between t wo different cells can be divided into three types,including spatial adjacency,neighbour hood and complicated separation.Ba sed on tradition-al ideas,it is impossible to settle CA’s restrictions completely.RDB-based CA is an academic experiment,in which some fields ard desighed to describe the essential information needed to define and select a cell’s neighbour.The culture innovation diffusion system has mul tiple forms of space diffusion and in herited characteristics that the RD B-based CA is capable of simulating more effectiv ely.Finally this paper details a successful case study on the diffusion o f fashion wear trends.Compared to the original CA,the RDB-based CA is a more natural and efficient representation of human k nowl-edge over space,and is an effective t ol in simulation complex systems that have multiple forms of spatial diff usion.展开更多
文摘The performance of a material is directly affected by its microstructural development during the solidification phase. Discrete cellular automaton (CA) models are widelyused in materials science to simulate and predict microstructural growth. This review comprehensively explains the developments and applications of CA in solidification structure simulation, including the theoretical underpinnings, computational procedures, software development, and recent advances. Summarizes the potential and limitations of cellular automata in understanding microstructure evolution during solidification, explores the evolution of microstructures during solidification, and adds to our existing knowledge of cellular automaton theory. Finally, the research trend in simulating the evolution of the solidification microstructure using cellular automaton theory is explored.
基金supported by the Ministry of Education,Culture,Research,and Technology Directorate General of Higher Education,Research,and Technology grant number[2147/UN2621/PN/2022].
文摘The simultaneous increase in development in Pesawaran Regency is closely correlated with the intense competi-tion for land use.However,low policy implementation effectiveness has led to construction beyond designated spatial plan.The study used a quantitative survey using Landsat images in 2016,2019,and 2022.The data analysis techniques used geographic information systems integrated with Artificial Neural Network(ANN)and Cellular Automata(CA)models.This study aims to predict land-use change in 2031,evaluate its alignment with spatial planning,and provide guidance for controlling land-use change.The results showed that there has been an increase in land use.In 2019,built-up land reached 7,069.65 Ha.The model shows its ability to predict land simulation and transformation,where it is predicted that built-up land in 2031 will experience an increase of up to 40.10%,so development and change cannot be avoided every year.This study also suggests that decision-makers and local governments should reconsider spatial planning strategies.This study shows that there have been many land use changes from 2016 to 2022.The model shows its ability to predict simulation and land transformation.When using the model,there are many changes in the land use area in 2031.This is due to wet agricultural land turning into built-up land by almost 70%.This study shows that road network influence land-use change.The cellular automata model managed to capture the complexity with simple rules.Predictions for future research should focus on conserving wetlands and primary forests.
基金supported the National Natural Science Foundation of China(42071371)the National Key R&D Program of China(2018YFB0505400).
文摘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.
基金supported by the Shanghai Science and Technology Committee (22511105500)the National Nature Science Foundation of China (62172299, 62032019)+2 种基金the Space Optoelectronic Measurement and Perception LaboratoryBeijing Institute of Control Engineering(LabSOMP-2023-03)the Central Universities of China (2023-4-YB-05)。
文摘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.
基金supported by the Kempe post-doc fellowship via Project No.SMK21-0061,Sweden.Additional support was provided by the Wallenberg AI,Autonomous Systems and Software Program(WASP)funded by Knut and Alice Wallenberg Foundation.
文摘The development of the Internet of Things(IoT)technology is leading to a new era of smart applications such as smart transportation,buildings,and smart homes.Moreover,these applications act as the building blocks of IoT-enabled smart cities.The high volume and high velocity of data generated by various smart city applications are sent to flexible and efficient cloud computing resources for processing.However,there is a high computation latency due to the presence of a remote cloud server.Edge computing,which brings the computation close to the data source is introduced to overcome this problem.In an IoT-enabled smart city environment,one of the main concerns is to consume the least amount of energy while executing tasks that satisfy the delay constraint.An efficient resource allocation at the edge is helpful to address this issue.In this paper,an energy and delay minimization problem in a smart city environment is formulated as a bi-objective edge resource allocation problem.First,we presented a three-layer network architecture for IoT-enabled smart cities.Then,we designed a learning automata-based edge resource allocation approach considering the three-layer network architecture to solve the said bi-objective minimization problem.Learning Automata(LA)is a reinforcement-based adaptive decision-maker that helps to find the best task and edge resource mapping.An extensive set of simulations is performed to demonstrate the applicability and effectiveness of the LA-based approach in the IoT-enabled smart city environment.
文摘[Objective]Urban floods are occurring more frequently because of global climate change and urbanization.Accordingly,urban rainstorm and flood forecasting has become a priority in urban hydrology research.However,two-dimensional hydrodynamic models execute calculations slowly,hindering the rapid simulation and forecasting of urban floods.To overcome this limitation and accelerate the speed and improve the accuracy of urban flood simulations and forecasting,numerical simulations and deep learning were combined to develop a more effective urban flood forecasting method.[Methods]Specifically,a cellular automata model was used to simulate the urban flood process and address the need to include a large number of datasets in the deep learning process.Meanwhile,to shorten the time required for urban flood forecasting,a convolutional neural network model was used to establish the mapping relationship between rainfall and inundation depth.[Results]The results show that the relative error of forecasting the maximum inundation depth in flood-prone locations is less than 10%,and the Nash efficiency coefficient of forecasting inundation depth series in flood-prone locations is greater than 0.75.[Conclusion]The result demonstrated that the proposed method could execute highly accurate simulations and quickly produce forecasts,illustrating its superiority as an urban flood forecasting technique.
基金supported by the Malaysia-Japan International Institute of Technology(MJIIT),Universiti Teknologi Malaysia.
文摘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.
文摘In this paper, we use some programing tools and algorithms for solving system of word equation for regular languages. There are many possibilities for presentation of regular languages such as grammars, finite automata, rewriting systems and so on. Some of these systems is presented by system of computational discrete algebra GAP and the possibilities of presentation now in some systems interactive theorem provers (Isabelle, Coq). This computer system can give to detailed understanding of solution of system of word equation, compared the languages and regular expressions of the languages.
基金Supported by Supported by National Natural Science Foundation of China (No.60074014)
文摘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.
文摘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.
文摘Wireless Multimedia Sensor Network (WMSN) is an advancement of Wireless Sensor Network (WSN) that encapsulates WSN with multimedia information like image and video. The primary factors considered in the design and deployment of WSN are low power consumption, high speed and memory requirements. Security is indeed a major concern, in any communication system. Consequently, design of compact and high speed WMSN with cryptography algorithm for security, without compromising on sensor node performance is a challenge and this paper proposes a new lightweight symmetric key encryption algorithm based on 1 D cellular automata theory. Simulations are performed using MatLab and synthesized using Xilinx ISE. The proposed approach supports both software and hardware implementation and provides better performance compared to other existing algorithms in terms of number of slices, throughput and other hardware utilization.
基金supported by the National Natural Science Foundation of China(61174094)the Tianjin Natural Science Foundation of China(13JCYBJC1740014JCYBJC18700)
文摘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.
基金supported by the National Natural Science Foundation of China (41130530,91325301,41401237,41571212,41371224)the Jiangsu Province Science Foundation for Youths (BK20141053)the Field Frontier Program of the Institute of Soil Science,Chinese Academy of Sciences (ISSASIP1624)
文摘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.
文摘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.
基金supported by the National Natural Science Foundation of China(Grant No.50478088)the Natural Science Foundation of Hebei Province,China(Grant No.E2015202266)
文摘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.
基金supported by the National Basic Research Program of China (No. 2006CB705401)the National Natural Science Foundation of China (No.51075270)the Natural Science Foundation of the Jiangsu Higher Education Institutions of China (No.10KJD460003)
文摘A modified cellular automata （CA） model of dynamic recrystallization （DRX） and a flow stress-based nucleation parameter identification method have been developed. In the method, the modified CA model, which takes the role of deformation degree on nucleation behavior into consideration, is coupled with an adaptive response surface model （ARSM） to search for the optimum nucleation parameter. The DRX behavior of an oxygen free high conductivity （OFHC） copper with different initial grain sizes has been taken as an example to validate the model. Good agreement is found between the simulated and the experimental results, which demonstrates that the new method can effectively improve the simulation accuracy.
基金Project supported by the National Basic Research Program of China(Grant No.2012CB719705)the National Natural Science Foundation of China(Grant Nos.91224008,91024032,and 71373139)
文摘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.
文摘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.
基金Under the auspices of the National Natural Science Foundation of China(No.40071071)
文摘This paper presents a development o f the extended Cellular Automata9CA),based on relational databases(RDB),to model dynamic interactions amon g spatial objects.The integration o f Geographical Information System(GIS)and CA has the great advantage of simu lationg geographical processes.But standard CA has some restrictions i n cellular shape and neighbourhood and neighbour rules,which restrict the CA’s ability to simulate complex,real world environ-ments.This paper discusses a cell’s spatialrelationbasedonthe spatialobject’s geometricalandmon-geometricalc haracter-istics,and extends the cell’s neighbour definition,and considers that the cell’s neighbour lies in the forms of not on ly spa-tial adjacency but also attribute co rrelation.This paper then puts forw ard that spatial relations between t wo different cells can be divided into three types,including spatial adjacency,neighbour hood and complicated separation.Ba sed on tradition-al ideas,it is impossible to settle CA’s restrictions completely.RDB-based CA is an academic experiment,in which some fields ard desighed to describe the essential information needed to define and select a cell’s neighbour.The culture innovation diffusion system has mul tiple forms of space diffusion and in herited characteristics that the RD B-based CA is capable of simulating more effectiv ely.Finally this paper details a successful case study on the diffusion o f fashion wear trends.Compared to the original CA,the RDB-based CA is a more natural and efficient representation of human k nowl-edge over space,and is an effective t ol in simulation complex systems that have multiple forms of spatial diff usion.
