Narrow Results

With the development of the economy, the demand for 3D model animation in fields such as games, film and television, and education is constantly increasing. At present, many 3D model animations are built based on genetic algorithm. However, due to the slow convergence speed and poor local search ability of genetic algorithm, this model still has many shortcomings. Therefore, a cellular automaton is used to optimize the genetic algorithm, and a cellular genetic algorithm is proposed. It is integrated with WebAR technology, which has the characteristics of lightweight and low cost, aiming to improve the separation and dynamic binding mechanism of 3D model animation. Experimental analysis showed that when the number of chromosome genes was 90, the vertex gene value of the research algorithm was the highest, ranging from 281 to 501, and the convergence was the highest. The file size of the model dataset based on the research method was reduced by 50% compared with before. The FPDT based on the animation separation and dynamic binding mechanism was decreased by 15.2 s on average. To sum up, it can be seen that the designed 3D model animation is more detailed and the animation dynamic is also the best. This research method can significantly improve the separation and dynamic binding mechanism of 3D model animation. This research will improve the integration of genetic algorithm and WebAR technology, which can not only effectively deal with complex 3D models and animations, but also has broad application prospects.

Conway’s Game of Life is the most well-known cellular automaton. The universe of the Game of Life is a 2-dimensional array of cells, each of which takes two possible states, alive or dead. The state of every cell is repeatedly updated according to those of eight neighbors. A cell will be alive if exactly three neighbors are alive, or if it is alive and two neighbors are alive. The main contribution of this paper is to develop several acceleration techniques for simulating the Game of Life using a GPU as follows: (1) the states of 32/64 cells in 32/64-bit words (integers) and the next states are computed by the Bitwise Parallel Bulk Computation (BPBC) technique, (2) the states of cells stored in 2 words are updated at the same time by a thread, (3) warp shuffle instruction is used to directly transfer the current states stored in registers, and (4) multi-step simulation is performed to reduce the overhead of data transfer and invoking CUDA kernel. The experimental results show that, the performance of our GPU implementation using GeForce GTX TITAN X is 1350×10⁹ updates per second for 16K-step simulation of 512K ×512K cells stored in the SSD. Since Intel Core i7 CPU using the same technique performs 13.4×10⁹ updates per second, our GPU implementation for the Game of Life achieves a speedup factor of 100. Thus, these techniques work very efficiently on a GPU.

One-dimensional cellular automata are known to be able to present complex behaviors. In some cases, their evolution may be understood as movings, collisions, or creations of particles. In the case of the special Wolfram's rule 54, Boccara has previously pointed out basic particles. In this paper, we introduce a group which allows the formal study of interactions between these particles. Coming back to the complexity of rule 54 and using the new algebraic classification of Rapaport, we prove that rule 54 is not simple.

The two-dimensional ferromagnetic Blume–Capel model is simulated on a cellular automaton, which based on the Creutz cellular automaton for square lattice. The values of the critical temperature and the static critical exponents are estimated within the framework of the finite-size scaling theory for 0 ≤ D/J ≤ 1.5. The results are compatible with the universal Ising critical behavior.

Given a continuous function f(x), suppose that the sign of f has only finitely many discontinuous points in the interval [0, 1]. We show how to use a sequence of one-dimensional deterministic binary cellular automata to determine the sign of f(ρ) where ρ is the (number) density of 1s in an arbitrarily given bit string of finite length provided that f satisfies certain technical conditions.

An important global property of a bit string is the number of ones in it. It has been found that the parity (odd or even) of this number can be found by a sequence of deterministic, translational invariant cellular automata with parallel update in succession for a total of O(N²) time. In this paper, we discover a way to check if this number is divisible by three using the same kind of cellular automata in O(N³) time. We also speculate that the method described here could be generalized to check if it is divisible by four and other positive integers.

Recently, we have proposed a Euler–Lagrange transformation for cellular automata (CA) by developing new transformation formulas. Applying this method to the Burgers CA (BCA), we have succeeded in obtaining the Lagrange representation of the BCA. In this paper, we apply this method to multi-value generalized Burgers CA (GBCA) which include the Fukui–Ishibashi model and the quick-start model associated with traffic flow. As a result, we have succeeded in clarifying the Euler–Lagrange correspondence of these models. It turns out moreover that the GBCA can naturally be considered as a simple model of a multi-lane traffic flow.

Considering the effects of different factors on the stochastic delay probability, the delay probability has been classified into three cases. The first case corresponding to the brake state has a large delay probability if the anticipant velocity is larger than the gap between the successive cars. The second one corresponding to the following-the-leader rule has intermediate delay probability if the anticipant velocity is equal to the gap. Finally, the third case is the acceleration, which has minimum delay probability. The fundamental diagram obtained by numerical simulation shows the different properties compared to that by the NaSch model, in which there exist two different regions, corresponding to the coexistence state, and jamming state respectively.

A multi-speed cellular automaton is used to simulate the escaping pedestrian flow in a corridor under the open boundary condition. Keeping the total number of people fixed, and pedestrians moving in different speeds, we studied the evolution of pedestrian flow by varying the parameters of system size. The relationships between parameters of system size and the transition time are discussed in this paper. It is found that the transition time t_c closely depends on the width (W) of corridor and the maximum speed V_max of people, it scales as t_c∝W^-0.80±0.06, and , in which α increases with the width of doors. However, the width of doors and the strength of drift have little influence on the transition time.

A new cellular automaton model for pedestrian dynamics based on motor schema is presented. Each pedestrian is treated as an intelligent mobile robot, and motor schemas including move-to-goal, avoid-away and avoid-around drive pedestrians to interact with their environment. We investigate the phenomenon of many pedestrians with different move velocities escaping from a room. The results show that the pedestrian with high velocity have predominance in competitive evacuation, if we only consider repulsion from or avoiding around other pedestrians, and interaction with each other leads to disordered evacuation, i.e., decreased evacuation efficiency. Extensions of the model using learning algorithms for controlling pedestrians, i.e., reinforcement learning, neural network and genetic algorithms, etc. are noted.

We have modified the transition probability calculations in the lattice gas model of biased-random walkers. Adopting the collision-avoided rules, a new cellular automata model of pedestrian flow which updates in parallel is established. Using this model, we have simulated and studied the evacuation process of people in a hall. The spatial configuration of people is presented, which is more consistent with the scene of reality than that presented in the lattice-gas model. It is found that the escape time t_c closely depends on the width of the door and the initial density of people, it scales as t_c ∝ W^-0.55±0.01 and t_c ∝ ρ^0.98±0.01 ≈ ρ.

The evacuation process under emergency is studied by means of experiments and simulations, focusing on the influence of the environment information. A revised cellular automaton model in which environment information is considered as "static information" (building structure, spatial distance, etc.) and "dynamic information" (sounds of fire alarm, etc.) is introduced. Two scenarios, including evacuation with and without visibility in a classroom, are studied to investigate the different influence of the two kinds of information on human behavior. The experimental and simulation results demonstrate that:

(1) to intensify the spatial distance information can reduce the evacuation time;

(2) the spatial distance is not the only decisive factor especially in evacuation without visibility because the sound information, which is ignorable in evacuation with visibility, is playing a more important role under this condition;

(3) the intensity of static information can reflect evacuees' familiarity of the environment;

(4) the model can reproduce the experiments well, and the simulation method is useful for further study of the crowd movement simulation.

The spin-1 Ising Blume–Emery–Griffiths (BEG) model has been simulated using a cellular automaton (CA) algorithm improved from the Creutz cellular automaton (CCA) for a face-centered-cubic (fcc) lattice. The ground state diagram (k, d) of the fcc BEG model has ferromagnetic (F), quadrupolar (Q), and staggered quadrupolar (SQ) structure regions. The simulations have been made in the SQ region for the parameter values in the intervals -24 ≤ d = D/J < 0 and -3 ≤ k = K/J ≤ 0. The phase diagrams on the (kT_C/J, d) and the (kT_C/J, k) planes have been obtained through k = -3 and d = -4 lines, respectively. The SQ region separates into five regions (A₃ B(a), A₃ B(f), AB (type-I), AB (type-II), and AB₃(f)) which have the different stoichiometric Cu–Au type structures.

The inertial driving behavior, although observed in real traffic, has not been deeply understood in most previous studies. In this paper we propose a cellular automata model of the traffic on a single lane highway which considers the drivers' inertial behavior under different driving circumstances. Simulation results show that our model can capture such important features of the traffic as jam formation, phase transition and metastable states. Some of other models, like the slow-to-start model, can be derived from our model.

This article considers an extension to the standard framework of cellular automata by implementing memory capability in cells. It is shown that the important block HPP rule behaves as an excellent classifier of the density in the initial configuration when applied to cells endowed with pondered memory of their previous states. If the weighing is made so that the most recent state values are assigning the highest weights, the HPP rule surpasses the performance of the best two-dimensional density classifiers reported in the literature.

To depict the mixed traffic flow consisting of motorized (m-) and non-motorized (nm-) vehicles, a new cellular automaton model is proposed by combining the NaSch model and the BCA model, and some rules are also introduced to depict the interaction between m-vehicles and nm-vehicles. By numerical simulations, the flux-density relations are investigated in detail. It can be found that the flux-density curves of m-vehicle flow can be classified into two types, corresponding to small and large density regions of nm-vehicles, respectively. In small density region of nm-vehicles, the maximum flux as well as the critical density decreases with the increase of nm-vehicle density. Similar characteristics can also be found in large density region of nm-vehicles. However, compared with the former case, the maximum flux is much lower, the phase transition from free flow to congested flow becomes continuous and thus the corresponding critical points are non-existent. The flux-density curves of nm-vehicle flow can also be classified into two types. And interestingly, the maximum flux and the corresponding density decrease first and keep constant later as the density of m-vehicle increases. Finally, the total transport capacity of the system is investigated. The results show that the maximum capacity can be reached at appropriate proportions for m-vehicles and nm-vehicles, which induces a controlling method to promote the capacity of mixed traffic flow.

Many cellular automaton models (CA models) have been applied to analyze traffic flow. When analyzing multilane traffic flow, it is important how we define lane-changing rules. However, conventional models have used simple lane-changing rules that are dependent only on the distance from neighboring vehicles. We propose a new lane-changing rule considering velocity differences with neighboring vehicles; in addition, we embed the rules into a variant of the Nagel–Schreckenberg (NaSch) model, called the S-NFS model, by considering an open boundary condition. Using numerical simulations, we clarify the basic characteristics resulting from different assumptions with respect to lane changing.

In this paper, we propose a novel slope cellular automaton (CA) model to depict some physical properties of traffic flow with slopes. In our model, we present the effect of slopes on the acceleration/deceleration capabilities and safety distance of the vehicles in highways as in real traffic situations. By numerical simulations, we investigate the dependence of the vehicle capacities in highways on the length and grade of slopes. It is shown that the larger the slope grade, the more significant the effect of slopes on the traffic flow is. Especially when the slope grade is beyond a certain value (i.e. |σ| > 3%), the effect of slopes on traffic flow becomes quite markedly.

The stochastic optimal velocity (SOV) model, which is a cellular automata model, has been widely used because of its good reproducibility of the fundamental diagram, despite its simplicity. However, it has a drawback: in SOV, a vehicle that is temporarily stopped takes a long time to restart. This study proposes a revised SOV model that suppresses this particular defect; the basic concept of this model is derived from the car-following model, which considers the velocity gap between a particular vehicle and the preceding vehicle. A series of simulations identifies the model parameters and clarifies that the proposed model can reproduce the three traffic phases: free, jam, and even synchronized phases, which cannot be achieved by the conventional SOV model.

Self-reproduction on asynchronous cellular automata (ACAs) has attracted wide attention due to the evident artifacts induced by synchronous updating. Asynchronous updating, which allows cells to undergo transitions independently at random times, might be more compatible with the natural processes occurring at micro-scale, but the dark side of the coin is the increment in the complexity of an ACA in order to accomplish stable self-reproduction. This paper proposes a novel model of self-timed cellular automata (STCAs), a special type of ACAs, where unsheathed loops are able to duplicate themselves reliably in parallel. The removal of sheath cannot only allow various loops with more flexible and compact structures to replicate themselves, but also reduce the number of cell states of the STCA as compared to the previous model adopting sheathed loops [Y. Takada, T. Isokawa, F. Peper and N. Matsui, Physica D227, 26 (2007)]. The lack of sheath, on the other hand, often tends to cause much more complicated interactions among loops, when all of them struggle independently to stretch out their constructing arms at the same time. In particular, such intense collisions may even cause the emergence of a mess of twisted constructing arms in the cellular space. By using a simple and natural method, our self-reproducing loops (SRLs) are able to retract their arms successively, thereby disentangling from the mess successfully.