89 results about "Cellular automaton" patented technology

The invention provides a sorting method of vehicle-mounted laser scanning point cloud data of urban ground objects. The method mainly comprises two steps as follows: original vehicle-mounted laser scanning point cloud data are subjected to road surface filtering in combination of vehicle tracking data, and point cloud data of a road surface are separated from point cloud data of other ground objects; and the obtained point cloud data of other ground objects are further identified to be different ground object types. In the road surface filtering algorithm, the vehicle tracking data are combined, and a histogram statistic and analytical method is introduced, so that the self-adaption degree, the automation degree and the accuracy of the filtering algorithm of the road surface in an urban environment are improved greatly; and in the sorting algorithm of other ground objects, a point cloud feature image sorting method based on the maximum elevation value is improved, mistakenly sorted point cloud data are corrected with a cellular automaton technology, and a new thought and method are provided for the sorting algorithms of vehicle-mounted laser scanning point cloud data.

The invention discloses a saliency object detection method based on Faster R-CNN. The method comprises the steps of first performing multi-scale segmentation on an image, then outlining possible saliency objects using the Faster R-CNN, establishing an object analogue map, thereafter distributing a foreground specific gravity to a superpixel via foreground connectivity, then obtaining round and smooth saliency maps in combination with specific gravities of a foreground and a background using a saliency optimization technology, and at last performing fusion using an MCA (Multi-layer Cellular Automata) to obtain a final saliency map. The segmentation is performed on an input image on three scales using a superpixel segmentation algorithm, and the superpixel segmentation algorithm is to aggregate adjacent and similar pixel points into different sizes of image areas according to low-level characteristics such as a color, a texture and a brightness, such that the complexity of saliency detection can be effectively reduced; and by taking different scales of segmented images as a layer of cells and performing fusion on the different scales of superpixel segmented images using the MCA, theconsistency of an image saliency detection result is guaranteed.

Crashworthiness designing of a structure using a Hybrid Cellular Automata (HCA) algorithm where field states are computed using finite element analysis (FEA) and the material distribution of the structure is updated at each iteration using cellular automata method. The HCA algorithm optimizes the topology of the structures to achieve certain performance within the limits of various constraints applied to ensure crashworthiness of the structures. The HCA algorithm may also be applied to designing of structures to be fabricated by an extrusion method having the same cross section along the direction of extrusion or stamped structures having thickness varying across the structure.

The invention provides an indoor evacuation simulating optimization method based on a potential energy driving cellular ant colony algorithm. The method is a personnel evacuation behavior simulating optimization method based on colony intelligence, a two-dimensional cellular automaton mathematic model in accordance with an actual scene is mainly built, personnel evacuation behaviors are simulated by using the cellular ant colony algorithm, and personnel paths are judged and selected according to a potential energy evaluation criterion of an artificial potential energy field, so that the method is more consistent to a true scene evacuation rule, the evacuation efficiency is improved, and a reasonable evacuation scheme is provided.

The present invention discloses a rule mining based flight arrival and departure cooperative scheduling method and belongs to the technical field of flight scheduling. The method comprises the steps of: firstly, analyzing historical operating data of a target airport, performing mapping modeling by applying an artificial neural network, and extracting out operation modes and rules of the airport under weather conditions in different seasons; and then establishing a flight arrival and departure cooperative scheduling model, designing a dynamic optimization algorithm by combination with a cellular automaton, performing optimized sequencing on arrival and departure flights, and inputting an updated scheduling scheme according to real-time data. The method takes the characteristics and interacting factors of the arrival and departure processes of the flights into comprehensive consideration; and meanwhile, with reference to different operation modes, obtained by performing rule mining on the historical operating data of the target airport, of the target airport under different conditions, a flight arrival and departure scheduling model that is more comprehensive and closer to actual scheduling is established, so that the arrival and departure of the flights can be ensured to more efficient.

A signature capture and analysis system suitable for use in a high performance computer graphics system is described. The system employs a distributed network of signature analysis registers (SARs) which may be configured to capture and accumulate information from one or more channels of data over pre-defined periods of time. The SARs may be so distributed to allow for the isolation of faults to a sub-system level. The signature values developed in these SARs are, in some cases pre-seeded, and may include contributions from both data and control signals. Checking of the signature values against known good or expected outcomes is provided for. In some cases the SARs may be implemented as linear hybrid cellular automatons.

The invention discloses a random number generator circuit based on real-time data streams and pseudo random numbers, comprising an m-bit linear feedback shift register circuit, an n-bit cellular automaton shift register circuit, a random counter circuit, a random data acquisition circuit, a logic exclusive-OR circuit and an HASH diffusion circuit, wherein the random data acquisition circuit outputs random bit streams; the logic exclusive-OR circuit processes the obtained original random bit streams and then generates internal random bit streams; and the HASH diffusion circuit processes the internal random bit streams to form and output random bits with different places, which are used for a crypto module. The invention can generate reliable and unpredictable random numbers, thereby enhancing the safety and the reliability of products.

The invention discloses a highway traffic flow cellular automaton simulation method. Multi-segment vehicle densities and lane number change are fused, and a simulation model of a cellular automaton established based on the real highway flow condition, multi-segment different road characteristics and vehicle conditions is established. Detected flow is mapped to road densities for road state simulation, and real measured data and the vehicle number of the cellular automaton are mapped. A novel boundary condition of the cellular automaton is provided, the lane number change of a highway is considered, the densities of multiple segments are different, work of vehicle information transfer between lanes is involved, the traffic flow simulation result is more precise through inspection, and actual traffic flow data can be simulated.

The invention discloses a crowd-evacuation simulating method based on an extended BDI model. The crowd-evacuation simulating method, on the basis of the extended BDI model and with the cellular automaton and multi-agent technologies, can simulate the crowd evacuation in a public place in emergency (e.g. a fire, etc.) according to the plane layout of a building. The extended BDI structure provides a certain support for the factors in the spiritual aspect, so the method fully considers the influence of the psychological state of the people on the evacuation in the process of crowd simulation; the method is used for simulation of a crowd with incomplete environmental information; and the method can conform better to the practice. The method has an extensive application prospect in simulation studies of safe and fast evacuation of a large crown in a building and can test design proposals or responding proposals, and therefore the invention is an economical and feasible crowd-evacuation simulating method.

The invention relates to a clustering wireless sensor network malicious program propagation model based on evolution game, and the model comprises three system models: a wireless sensor network model, an improved two-dimensional cellular automaton malicious program propagation model, and a multi-person evolution game model. On the basis of considering the node distribution density of a wireless sensor network topological structure, the invention proposes the clustering wireless sensor network cellular automaton malicious program propagation model based on multi-person evolution game.

A signature capture and analysis system suitable for use in a high performance computer graphics system is described. The system employs a distributed network of signature analysis registers (SARs) which may be configured to capture and accumulate information from one or more channels of data over pre-defined periods of time. The SARs may be so distributed to allow for the isolation of faults to a sub-system level. The signature values developed in these SARs are, in some cases pre-seeded, and may include contributions from both data and control signals. Checking of the signature values against known good or expected outcomes is provided for. In some cases the SARs may be implemented as linear hybrid cellular automatons.

The invention discloses an image classifying method based on transfer learning a multiple attractor cellular automata (MACA) and mainly solves the problems that an existing image classifying method based on transferring cannot avoid the empty basin appearance, the similarities of a source domain sample and a target domain sample are not accurately calculated, the effect of transferring from the source domain sample to the target domain sample is poor, and the classification accuracy is low. The image classifying method comprises the steps: (1) performing image data pre-processing; (2) training a multiple attractor cellular automata (MACA) tree in a source domain space; (3) dividing a target domain training set; (4) constructing a local mode space training set; (5) training a multiple attractor cellular automata (MACA) tree in a local mode space; (6) generating a target domain multiple attractor cellular automata (MACA) tree. The image classifying method has the advantages of strong generalization ability and high classifying accuracy and effectively solves the problems that the existing image classifying method cannot avoid the empty basin appearance and poor transferring effect.

The invention discloses an area-of-interest detection method based on background prior and a foreground node. The method comprises steps of 1) by use of SLIC algorithm, segmenting an original image into super-pixels; 2) by use of K-means clustering algorithm, carrying out clustering on boundary super-pixels, according to a clustering result, constructing a global color difference matrix and a global space distance matrix, fusing the global color difference matrix and the global space distance matrix into a saliency map based on background prior, and finally, by use of a single-layer cellular automaton, primarily optimizing the saliency map based on the background prior; 3) carrying out adaptive threshold segmentation on the saliency map based on the background prior so as to obtain a foreground node, according to a contrast ratio relation, obtaining a saliency map based on the foreground node, and by use of the biased Gauss filtering, carrying out optimization; and 4) fusing the saliency map based on the background prior and the saliency map based on the foreground node, obtaining the final saliency map. According to the invention, the method is used in an image processing processand can be widely applied in visual working field like visual tracking, image segmentation and target re-positioning.

The invention claims a new method of generating financial trading alerts, improved buying and selling decisions, and financial controls using cellular automata. An array known as a Virtual Trading Neighborhood (“VTN”) is comprised of cells that comprise financial data received from active traders including intentions to buy and sell and also actual buy and selling actions. The VTN further comprises a cellular automata cell that is programmed with preselected rules. The cellular automata cell generates an output based on the prior trading actions of the surrounding neighbor cells of the VTN. The resulting buy or sell trading alerts are provided to authorized users to manage financial trading risks and increase profits.

The invention relates to a city expansion multi-scenario simulation cellular automaton method based on a cross entropy optimizer, which comprises the following steps: 1) supervising and classifying satellite remote sensing images to obtain a land utilization classification map, and establishing spatial variable factor data; 2) acquiring effective sample points in the research area based on the spatial variable factor data; 3) establishing a CA city expansion simulation prototype model, and acquiring CA parameters based on the effective sample point data; 4) establishing a related objective function for optimizing CA parameters, and optimizing the CA parameters by using a cross entropy optimizer; 5) establishing a CA conversion rule, and obtaining a conversion probability graph; 6) establishing an urban expansion simulation CACEO model, and simulating and predicting urban expansion dynamics and future possible scenes; and 7) performing precision evaluation on the CACEO model and the simulation prediction result thereof, and outputting and storing the simulation result. Compared with the prior art, the method provided by the invention effectively optimizes the CA model and realizes multi-target city expansion scene prediction through objective weight determination.

The invention discloses an implementation method for a traffic flow cellular automaton model on the basis of intelligent game playing. The method comprises the following steps that: S1: before simulation is carried out, firstly setting simulation time length, a parameter [Lambda], a maximum speed value vmax, and the preset numerical values of the numbers of front and rear roads of a merging area, length and a connection way; S2: before simulation is carried out, on a position of which the original point distance is equal to 0, on the basis of Poisson distribution, randomly generating a vehicle, and generating an initial speed on the basis of the Poisson distribution; S3: judging a position coordinate for the generated vehicle, determining the area of the position coordinate, and judging speed parameters in sequence according to different improvement rules; S4: combining the speed determined in the S3 to carry out position update and status flag update, and displaying and recording relevant data or images on the basis of the status flag; and S5: judging whether time achieves simulation time or not, and if the time does not achieve the simulation time, returning to the above steps. By use of the method, technical support is provided in fields including traffic detection and toll station design, and the method has a high practical value.

The invention discloses a method for detecting the consistency of different-source images, belonging to the technical field of computer image processing. The method comprises the following steps of: extracting contour images of the different-source images by adopting a two-dimensional cellular automaton model based on a morphological gradient method; acquiring characteristic points and descriptors of accelerating robust characteristics of the contour images; acquiring an initial matching dot pair set from the characteristic points of the accelerating robust characteristics; and screening out an accurately matched dot pair from the initial matching dot pair set. By using the method disclosed by the invention, the problem that the consistency cannot be directly detected from the different-source images by using a SURF algorithm is solved, and the detection speed and the accuracy rate are increased.

The invention relates to a land resource utilization change dynamic prediction model based on a GIS (Geographic Information System) and a processing method. The basic idea of the GeoCA (cellular automaton)-Landuse model on simulating land utilization change is as follows: vital signs of a land unit are introduced, and the dynamic evolution of land utilization type is simulated; starting with the overlay analysis and transition analysis of two time phase land utilization thematic maps, and combining with the metering model of land utilization change, the land utilization dynamic change feature is analyzed; the model control factors are analyzed according to a river system and traffic map in a researched area, and a control factor layer is constructed; combining with social economic data, the model parameters are adjusted, and reasonable neighbors and transformation rules are determined; and with the powerful spatial data processing and analyzing functions of the GIS, the land utilization change trend is simulated and predicted according to the change process of history land utilization. According to the model and the method disclosed by the invention, the simulation on dynamic trend and evolution rule of land utilization change is realized, and scientific basis is provided for scientific decision-making.

The invention discloses an image encryption method based on a chaotic system and a two-dimensional reversible cell automaton. The image encryption method comprises the steps of S1, processing the pixel values of the pixel matrix of a to-be-encrypted image in three directions of the horizontal direction, the vertical direction and the diagonal-line direction to obtain three coefficients; S2, adopting the three coefficients generated in the step S1 as the initial values of a Lorenz chaotic system so as to generate three sets of sequences Xk, Yk and ZK; performing the discretization to obtain animage shown in description; S3, by using the image obtained in the step S2, subjecting the pixels of the to-be-encrypted image to scrambling treatment; S4, by using the image obtained in the step S2,subjecting the scrambled image to the evolution treatment of a two-dimensional reversible cell automaton based on quadtree decomposition, repeatedly iterating and encrypting until an iteration requirement is met, and finally combining to form a ciphertext image. In this way, the problems in the prior art that ciphertext images of good hidden information effect cannot be obtained, the pixel valuesof a small ciphertext image in a secret key space is poor in distribution randomness and the correlation between adjacent pixels is high can be solved.

The present invention provides a multimedia encryption and decryption method based on a combination cell automatic machine, belonging to the multimedia encryption field. The encryption method comprises the following steps: a pseudo-random number generator generates two random sequences; a data preprocessing step is performed; a reversible cell automatic machine step is performed; and a cell automatic machine evolution step and a determination step are performed: it is determined whether the encrypted cryptograph accords with an avalanche effect or not, if the encrypted cryptograph does not accord with the avalanche effect, the cell automatic machine is employed for going on evolution, and if the encrypted cryptograph accords with the avalanche effect, the evolution is stopped to obtain the cryptograph of a plaintext. The present invention further provides a decryption method corresponding to the multimedia encryption method. The cell automatic machine is employed to perform multimedia encryption, the multimedia encryption and decryption method based on the combination cell automatic machine are simple and easy to programme, can perform association of the plaintext data and the encryption key, are higher in safety, and can effectively improve the safety of the encryption algorithm and the encryption efficiency.

The invention discloses an unmanned aerial vehicle remote sensing image quick splicing method and relates to the technical field of image processing. The method includes the following steps: step 1, performing low-altitude aerial photographing by an imaging system mounted on an unmanned aerial vehicle; step 2, performing multilevel grid partitioning on a mass of sequence images; step 3, splicing and mapping corresponding sequence images in each level in a level-by-level manner; step 4, processing at a later stage. A CUDA (compute unified device architecture) algorithm is introduced, so that image splicing speed can be greatly increased; a CA (cellular automaton) model is utilized to optimize an SIFT (scale invariant feature transform) feature point algorithm, so that image matching efficiency and accuracy are improved.

A golf ball 2 has a large number of dimples 8 and a land 10. A rugged pattern is formed on the surface of the golf ball 2 by these dimples 8 and the land 10. These dimples 8 are arranged in a random manner. Each dimple 8 has a crater shape. For designing the rugged pattern, a Cellular Automaton method is used. A fluctuation range Rh of the golf ball 2 that is an index indicating the aerodynamic characteristic during PH rotation is 1.9 mm or less. A fluctuation range Ro of the golf ball 2 that is an index indicating the aerodynamic characteristic during POP rotation is 1.9 mm or less. The absolute value of the difference dR between the fluctuation range Ro and the fluctuation range Rh is 0.4 mm or less.

The invention discloses a reverse transcriptase-deoxyribose nucleic acid (RT-DNA) cellular automaton-based image encryption method, and belongs to the field of DNA calculation and image encryption. A one-time pad is stored very difficultly, so that the use of the one-time pad is greatly limited. In order to overcome the disadvantage, the method takes natural DNA sequences as main keys, and the problem that the one-time pad is stored very difficultly is solved. The method comprises the following steps: first, generating a DNA matrix by utilizing the natural DNA sequences; then, generating a DNA template, and modifying the matrix formed by the natural DNA sequences by using the DNA template to obtain a key DNA matrix; and finally, regularly encrypting by using a newly defined RT-DNA cellular automaton to obtain an encrypted image. Experimental results show that the algorithm has higher safety and is easy to implement; moreover, the advantages of one-time pad-based and DNA cryptographic encryption-based encryption methods are retained effectively.

The invention discloses a method of simulating heavy gas diffusing among urban streets. The method is characterized in that an air mass model based on cellular automata is constructed and used for simulating the heavy gas, macro spreading of the gas is equivalently treated as multiple air masses, and an air mass motion model is generated by the cellular automata; features of the air masses are expressed as Qi (Vi0, xi and yi), influence radius D is determined by a Gaussian model and used for distinguishing concentration gradient diffusion Sd and free diffusion Sf, boundary contact conditions are constructed to assume a simulated urban environment, and the model is used to simulate ammonia diffusion in the atmospheric stable state. The method includes the steps of determining the influence radius D, the air mass automata model, changes in air mass motion speed, and the boundary contact conditions, setting model construction parameters, and simulating. The method is applicable to simulating gas diffusion and distribution under no action of wind when the urban street layout is complex.

The invention relates to a pedestrian evacuation simulation method based on an extended BDI (base diffusion isolation) model. The method is based on the extended BDI model, a cellular automaton and a multi-agent technology are adopted, and personnel evacuation in public places in emergency (such as a fire disaster) is simulated according to plane layout of the actual building. According to the method, influence of psychological states of personnel on evacuation is fully considered in a pedestrian simulation process, certain support is given to the spirit factor by virtue of an extended BDI system structure, and the method is more consistent with the reality when being applied to pedestrian simulation with incomplete environmental information. The method disclosed by the invention has a wide application prospect in personnel safety and rapid evacuation simulation research, can test a design scheme or a response scheme and is an economically feasible pedestrian evacuation simulation method.

The invention discloses a method for determining subway station structure stressed situation in fire. The method is characterized in that diffusion of a cellular automata (AC)-air mass model is simulated, gas macroscopic diffusion is taken as multiple air masses in an equivalent manner, the cellular automata is allowed to generate a moving model of the air masses, the air mass characteristics are expressed as Qi (Vi0, xi, yi), the Gaussian model is referred to determine an influence radius D to distinguish concentration gradient diffusion Sd and free diffusion Sf, the strength distribution of the wind in the streets is determined, a boundary contacting condition is constructed, a simulated city environment is assumed, the model is used to simulate ammonia diffusion when the wind speed is equal to 0.75 m / s, and influences on diffusion can be studied when the wind enters the streets in different moments. The method includes determining the influence radius D, determining an air mass automata model, determining change amount of air mass moving speed changes, determining wind field distribution, determining the boundary contacting condition, and setting model establishing parameters to simulate. The method can be applied in gas diffusion distribution simulation with the wind in conditions of complex arrangement of the city streets.

The invention provides an optical remote sensing image ship target detection method fusing space-frequency domain features. The method comprises the following steps: firstly, constructing an image feature map by utilizing a first-order gradient combination of a brightness feature map, a color feature map and brightness features of an image, carrying out non-overlapping partitioning on the image toobtain a plurality of image blocks, calculating a feature covariance matrix of each image block, and calculating a feature value between the feature covariance matrix of each image block and the feature covariance matrix of the whole image, and obtaining a normalized feature value graph as a spatial domain feature graph of the image; secondly, constructing quaternion features of the image, performing Fourier transform on the quaternion features, setting a transformed amplitude spectrum as 1 to leave phase information, performing inverse Fourier transform on the amplitude spectrum, and smoothing an inverse transform result by using a Gaussian filter to obtain a frequency domain feature map; and finally, fusing the feature maps of the spatial domain and the frequency domain by using a cellular automaton to obtain a final detection result. According to the method, the ship target can be rapidly and accurately detected from the remote sensing image with complex sea surface landform and cloud and fog backgrounds, the serious interference problem of the complex backgrounds on ship detection is solved, the ship detection effect under different complex backgrounds is good, and the application range is large.

The invention discloses a cellular automata model-based visual simulation method for a sewage purification process, and belongs to the fields of intelligent science and environmental engineering discipline. In the method, a three-dimensional lattice gas cellular automata model is adopted, a cube represents a cellular structure of the cellular automata, and the central nodes of the cube represent a static node and a diffusion node of a cell respectively. By designing states of different nodes and an evolution rule comprising a diffusion process, a reaction process and a settlement process, the purification process of sewage treatment is simulated dynamically. Through the simulation model established by the method, the dynamic evolution behaviors of the sewage treatment process by a sludge method can be described more intuitively, so the micro evolution behaviors of an active sludge system are more clear and easy to grasp; and by intuitively observing reaction changes, the BOD of effluent quality can be timely monitored, the control decision is conveniently guided in real time, and the design and dynamic simulation of a sewage treatment plant are convenient.

The invention provides an image encryption method based on a hybrid balanced second-order reversible two-dimensional cellular automata. The method comprises the following steps: pixel scrambling is performed on an R channel, a G channel and a B channel of an image I needing to be encrypted, and then an N*24M 0-1 matrix is obtained; the N*24M 0-1 matrix is divided into a plurality of 8*16 0-1 matrix sub groups; the hybrid balanced second-order reversible two-dimensional cellular automata is constructed, and an encryption iteration is performed on each matrix sub group; and the sub groups after encryption are combined to form an encrypted image.