115 results about "Population Distributions" patented technology

A memory using techniques to extract the data content of its storage elements, when the distribution of stored states is degraded, is presented. If the distribution of stored states has degraded, secondary evaluations of the memory cells are performed using modified read conditions. Based upon the results of these supplemental evaluations, the memory device determines the read conditions at which to best decide the data stored.

The invention discloses a station-based urban public traffic network optimized configuration method, which comprises the following steps of: optimized layout of bus stops under the constraint condition; calculation of trip generation of the bus stops; generation of candidate routes based on bus stop generation and route constraint condition; and genetic algorithm-based bus route optimization and index evaluation thereof. The station-based urban public traffic network optimized configuration method can search new routes based on the conventional routes, particularly realizes conventional public traffic network optimized layout in the presence of rapid transit. The station-based urban public traffic network optimized configuration method can directly evaluate the generation of the bus stops based on urban land utilization and population distribution, and also can directly utilize the generation and origin and destination data acquired by field transit survey. The system can be used for spatial distribution optimization of conventional metropolitan public traffic networks, and the efficiency for public traffic network regulation and layout is improved.

The invention provides a modeling method of the characteristics of population space-time dynamic moving based on multisource data fusion. The modeling method comprises: A. inputting map data, mobile phone locating data and floating vehicle data into a system and managing data organization according to requirements; B. establishing a spatial analysis model of the characteristics of population moving based on the mobile phone locating data and the floating vehicle data; C. applying the spatial analysis model of the characteristics of the population moving to carry out multisource data fusion of the map data, the mobile phone locating data and the floating vehicle data to obtain integrated information of the characteristics of the population moving; and D. analyzing the characteristics of various population moving according to the integrated information of the characteristics of the population moving and publishing an analyzed result by the geographic information system. The modeling method can acquire data of urban population space-time dynamic distribution and moving characteristics with large data amount, high quality and space-time characteristics, obtain basis of accurate population distribution and population moving characteristics, and provide decision-making supports for urban planning, land use planning, transportation planning and the like.

The invention discloses a dynamic prediction method of urban population refinement distribution based on depth learning, which comprises the following steps: 1, grid processing is carried out on the research area under the scale required by the research, the sample set is constructed, and the corresponding training set and test set are divided; 2, construct a prediction model of urban population distribution, and training that prediction model accord to historical data; 3, input that data of the distribution state of the urban population at the first j times as an input variable into the trainmodel in the step 2, and predicting and obtaining the distribution state of the urban population at a certain time in the future; The prediction model can predict all the regions divided by the urbangrid simultaneously. Convolution long-short memory network extracts spatial features through convolution structure, and acquires temporal features through long-short memory network structure.The bottom layer of the algorithm combines convolution neural network with long-short memory network to learn the high-dimensional spatio-temporal features of population distribution changes, and effectivelyfuses the spatial dimensions of time dimension, so as to improve the prediction accuracy.

The invention provides a dynamic population distribution density detecting method based on mobile phone positioning data, which is characterized by including the steps: dividing an analysis area coverage, needing population distribution density analysis, of a target city into different subareas, subsequently, establishing a virtual sensor network, obtaining the mapping relation between each subarea and the virtual sensor network, and obtaining a sum of the population in each subarea according to mobile phone signals. The dynamic population distribution density detecting method based on the mobile phone positioning data has the advantage that dynamic population distribution density detection is realized by fully depending on existing mobile communication network resources, utilizing mass mobile phone signal data as input and employing a distinguishing mechanism based on traveling track continuous tracking and stay duration time in the area. The method can be used for automatically detecting population distribution density information at a high frequency.

Methods and systems are disclosed for analyzing drive test data in conjunction with geographical data such as population distribution, geographical data as to wireless device usage, location of high-value customers, or other geographical data. The results of the analysis are reported to a human operator, such as a manager overseeing the drive test routes in a particular region. The results enable the operator to revise drive test routes, or select new routes, that provide improved measurement data for the wireless network.

A method of analyzing bubbles, cells, cell viability, or other particles or agglomerates in a process liquid contained in a vessel is provided. Images of bubbles, cells or other particles in the liquid are obtained in-situ with a vision probe extending through a wall of the vessel. The images are analyzed with image recognition software. The software measures at lease one of bubble, cell or particle size, mean diameter, surface area, flow rate, flow pattern, population distribution, viability, agglomerates or clumping, color change, viscosity, Sauter mean, ratio of surface area of bubbles relative to volume of bubbles, gas hold-up ratio of gas volume to volume of liquid, or interfacial area. The software distinguishes valid or viable bubbles, cells or particles that should be included in an analysis from invalid or non-viable bubbles, cells or particles that should not be included. The software can be configured to provide an analysis of the valid bubbles, cells or particles that fall within pre-set size and shape or viability parameters.

The invention relates to a population distribution spatial-temporal evolution and cognition method considering urban pattern characteristics. The population distribution spatial-temporal evolution andcognition method comprises the following steps: 1) carrying out scale conversion of multi-source and multi-scale remote sensing images; 2) extracting urban information based on the multi-source remote sensing image; 3) analyzing a multi-scale urban pattern-structure-function spatial-temporal relationship; 4) carrying out spatial-temporal correlation and coupling analysis on population distribution and urban characteristics; 5) constructing a population distribution simulation and prediction system considering urban pattern characteristics; and 6) carrying out spatial-temporal monitoring and cognition on population distribution in different scenes. The population distribution spatial-temporal evolution and cognition method has the advantages that population general survey and statistical data, multi-scale remote sensing data and multi-source land utilization / coverage data are used as data sources; the spatial-temporal characteristics and the evolution rule of the multi-scale urban spatial pattern are studied, and the population distribution spatial-temporal evolution model considering the town characteristic pattern is constructed, and the boundary of the population distribution density is finely described from the multi-level spatial scale, and a theoretical basis and a technical support are provided for population resource utilization and policy adjustment and social and economic resource allocation.

The invention relates to a method for determining the position and the agglomeration intensity of a city core area. A city built-up area geometric centroid, a city population distribution centroid and a city road traffic accessibility centroid are determined in a map for the city built-up area, every two centroids are connected together to obtain an area, namely the city core area position; and the size of the core area corresponds to the agglomeration intensity of each centroid, that is, the smaller the size of the core area is, the higher the agglomeration intensity of the center area is. The invention provides a technology method for determining the position of the city core area, with the characteristics of integrity of theoretical basis, objectivity of data selection, scientificalness of calculation method, pertinency of space types or the like. The method used for determining the position and the agglomeration intensity of the city core area has more scientificalness, rationality and operability and provides a good platform for research on the core area.

The invention discloses a population data spatialization method and system based on partition modeling, and a medium. The method comprises the following steps: collecting an original data source of aresearch area, which influences spatial distribution of population, and carrying out pre-processing; carrying out gridding processing on the data based on a geographic detector model, carrying out standardization processing after obtaining a population distribution influence index, and preliminarily screening out a population distribution influence factor; dividing the research area into a plurality of partitions, and respectively rescreening the population distribution influence factors of the partitions; and meanwhile, establishing a stepwise regression equation and a random forest model, performing precision comparative analysis on the population data spatialization result of each partition, selecting an optimal simulation result in each partition as a population data spatialization final result of each partition, and performing combination to obtain a population spatial distribution simulation schematic diagram. According to the method, the research area can be partitioned based onpartition modeling, the population data spatialization model of each partition can be constructed, and the accuracy and efficiency of population spatial distribution simulation are improved.

A memory using techniques to extract the data content of its storage elements, when the distribution of stored states is degraded, is presented. If the distribution of stored states has degraded, secondary evaluations of the memory cells are performed using modified read conditions. Based upon the results of these supplemental evaluations, the memory device determines the read conditions at which to best decide the data stored.

There is provided a system and method executed by a processor for estimating re-identification risk of a single individual in a dataset. The individual, subject or patient is described by a data subject profile such as a record in the dataset. A population distribution is retrieved from a storage device, the population distribution is determined by one or more quasi-identifying fields identified in the data subject profile. An information score is then assigned to each quasi-identifying (QI) value of the one or more quasi-identifying fields associated with the data subject profile. The assigned information scores of the quasi-identifying values for the data subject profile are aggregated into an aggregated information value. An anonymity value is then calculated from the aggregated information value and a size of a population associated with the dataset. A re-identification metric for the individual from the anonymity value is then calculated.

The invention provides a population gathering and dispersing detection method and device based on a corner clustering weighted area. The method comprises the first step of obtaining a video frame image, the second step of figuring out the foreground region of a population through the mixed Gaussian background modeling method, the third step of calculating corners of the image through the Harris corner detection algorithm, the fourth step of figuring out corners of a population region in the image by using the population foreground as a mask operator, the fifth step of performing clustering on the corners by using DBSCAN, the sixth step of calculating the corner area by using the weighting method, and the seventh step of calculating the slope of the changing curve of the corner area and performing incident recognition in combination with the number of class clusters. According to the method, the corners are used for describing population distribution, so that the inaccuracy of individual segmentation of the population is avoided; by performing clustering on the corners of the population, the overall density degree of the population in the current scene can analyzed according to the number of the class clusters of the population. Event decision is performed by calculating the slope of the changing curve of the corner area, so that the decision is simple and valid. In the process of the event decision, the changes of the number of the class clusters are further added for an auxiliary, and therefore the accuracy of a decision result is improved.

The invention discloses a demographic method and a demographic system for improving spatial resolution and updating a speed, wherein the method comprises a population database establishment step, a regional statistical analysis step, a spatial model analysis step and a grid demographic step, wherein the population database establishment step is used for storing relevant population data with a uniform spatial coordinate parameter, a data format and a metadata standard; the regional statistical analysis step is used for using a land utilization map as a main data source and carrying out spatial region statistical analysis according to a preset region to obtain land utilization area information of a minimal administrative division unit of the preset region; the spatial model analysis step is used for utilizing a population spatialization model to obtain population distribution information of the land utilization type in the minimal administrative division unit according to the land utilization area information of the minimal administrative division unit and demographic information of the minimal administrative division unit, and the grid demographic step is used for acquiring grid demographic information according to the population distribution information of the land utilization type and the land utilization area information.

The invention discloses a method and device for determining population distribution condition and relates to the technical field of communication. According to the method and the device, the precision of a population distribution determining process can be improved. The method provided by the method comprises the steps of obtaining communication base station working parameter data, terminal signals and user plane data, wherein the user plane data at least is used for expressing current location information of terminals; establishing grid graphs corresponding to various systems of networks according to the communication base station working parameter data; determining population density in each grid of the grid graphs according to the terminal signals, wherein the population density of each grid is a ratio of the number of the terminals in each grid and the area of each grid; superposing and segmenting the grid graphs corresponding to the various systems of networks, thereby obtaining a total grid graph; and coupling the current location information of the terminals obtained from the user plane data with the total grid graph, thereby obtaining the final population density of each grid in the total grid graph. The method and the device are applicable to a process of determining population distribution condition.

The invention discloses a GIS (Geographic Information System)-based region-meshed spatial population density computing method. According to the method, regional distribution characteristics of natural and socio-economic factors affecting population density are taken into full consideration, a statistical model is established in a manner that a village, namely a smallest regional administrative unit, serves as a unit, a research region is subjected to 100m*100m meshing, and the village-level statistical model is inverted to all meshes so as to describe population distribution by using the mesh units. The GIS-based region-meshed spatial population density computing method disclosed by the invention has the advantage that the spatial population analysis accuracy can be better improved.

The invention discloses a real-time urban population density and crowd flowing estimation method based on a smart phone. Bluetooth (or Wi-Fi) signals sent by adjacent measured mobile phones are scanned by a measuring mobile phone, and MAC addresses, signal intensity and timestamp information of the measured mobile phones are acquired; then data of an accelerometer, a gyroscope and a GPS sensor of the measuring mobile phone and position information of the measuring mobile phone are combined and are packaged to be sent to a central server so as to carry out statistics on the Bluetooth (or Wi-Fi) user number and distribution, and on the basis, population distribution in different regions of a city is determined; and then by combining an urban geographical structure, an urban population density distribution and urban crowd flowing map is generated.

The invention discloses an urban population distribution method based on building properties. The method comprises the specific steps that all building information and building periphery interest point information in an electronic map are obtained according to an urban longitude and latitude coordinate range; the electronic map is queried targeting a certain population statistic region, coordinates of the population statistic region are determined, and the gross population in the region is queried; all buildings in the population statistic region are screened out according to building locations and boundary angle point coordinates of the population statistic region; social activity properties of the buildings are inferred; population attraction coefficients, at different time unit areas, of the buildings of different social activity property types are set, and the population in the population statistic region is distributed into each building in the region to obtain building-grade population distribution; and all population statistic regions in a city are traversed to obtain urban population distribution. The method is applied to building-grade urban population distribution, the problem of rough urban population distribution scale can be effectively solved, and population distribution timing difference is reflected.

The invention discloses a hospitalizing recommendation method based on multi-source data analysis. The hospitalizing recommendation method includes registering information of medical and health organizations in a community through a network; collecting hospitalizing item information and location information of users, and sending hospitalizing recommendation requests; according to the hospitalizing recommendation requests, performing multi-source data fusion according to information of the medical and health organizations, current population distribution densities of the medical and health organizations and the location information of the users to acquire possibilities that the medical and health organizations are selected by the users within a certain time period; ranking the medical and health organizations according to values of the selection possibilities; releasing the top ten of the medical and health organizations and the geographical locations of the users by calling a map application programming interface; navigating according to routes of the medical and health organizations finally selected by the users on a map. The hospitalizing recommendation method achieves real-time hospitalizing recommendation, and provides the users with hospitalizing recommendations more suitable for users' needs and convenient navigation services.

The disclosure relates to technology for generating random numbers that are distributed by the population distribution. An empirical cumulative distribution function is constructed from random samples of the population, and a first random number is generated that is uniformly distributed over a first interval. A second interval in the empirical cumulative distribution function is found such that a range of values of the second interval cover the first random number. A second random number is then generated as an approximation to the random number drawn from the population, where the second random number is employed as part of a testing process.

A system and method for analyzing and classifying digital images as color, black-and-white, black-and-white with spot color, monochromatic, or monochromatic with spot color is provided. According to an embodiment of the present invention, a digital image is received and selected pixel values thereof are transformed from RGB color component values to a lightness component value and two color difference component values. The selected pixels are analyzed with respect to neutrality based upon the two color difference values and are grouped according to lightness value. The sample population distributions of each of the two color difference values are statistically analyzed for each group of pixels, and an image classification is determined by examination of the resulting statistics and derivatives thereof.

The invention discloses a high-confidence small-sample evaluation method for the reliability of explosives. According to the mensurability and reliability requirements of explosives, and under the premise that the performance parameters of explosives obey normal distribution or approximate normal distribution or the evaluated performance parameters after transformation obey normal distribution or approximate normal distribution, the parameters and uncertainty of a population distribution model are estimated based on small sample data and measurement uncertainty of product performance parameter quantitative measurement. In the absence of priori information and in the presence of statistical characteristics, the number of pieces of sample data needed by the method can be reduced to five under the condition that the confidence level is not less than 0.90 and the reliability of explosive performance evaluation is not less than 0.99 or above. The confidence level of evaluation results reaching required reliability is not lower than 97.5% of the prescribed confidence level.

The invention discloses a wild animal wireless monitoring analysis system and method based on a deep convolutional neural network. Image data of a moving object is acquired and a database is established; image calibration and preprocessing are performed; the deep convolutional neural network is trained in combination with an Adam optimization algorithm; wild animals are detected and identified; the appearance time of wild animals is prolonged; statistical analysis is carried out on places and a model is established; the problem of high labor intensity in the traditional method is solved; Artificial analysis time consumption is long, it is difficult to analyze and obtain the current situation of wild animal resources and the accurate situation of dynamic changes due to a large amount of redundant information and false identification information is solved, researchers can remotely obtain the individual characteristics and population distribution situation of wild animals in a monitored area in real time, and the working efficiency and the accuracy of analysis results are improved.

The invention discloses a method for constructing an earthquake disaster casualty population estimation model. The method includes a step of acquiring a plurality of historical seismic data includingmagnitudes of earthquakes, epicenters of the earthquakes, earthquake years, and earthquake casualty population, a step of determining the affection area of an earthquake of this time according to theearthquake magnitude and the epicenter of the earthquake, a step of determining a total population of the earthquake affection area according to population distribution data of the earthquake affection area, a step of obtaining a population casualty rate of the earthquake according to the earthquake casualty population and the total population of the earthquake affection area, and a step of establishing the earthquake disaster casualty population estimation mode characterized by an earthquake magnitude according to the magnitudes of historical earthquakes and corresponding earthquake population casualty rates. By using the model, the scope and casualty population of an earthquake disaster affection area can be quickly calculated at the first time of obtaining the earthquake magnitude, anda basis is provided for corresponding rescue measures through the rapid evaluation of a casualty after an earthquake disaster.

The invention provides an earthquake damage assessment system which comprises an earthquake information acquiring device, an earthquake source surrounding population distribution two-dimensional mesh generating device, an earthquake radius area population calculating device and a first earthquake damage assessment device. The earthquake information acquiring device is used for acquiring earthquake information in earthquake areas. The earthquake source surrounding population distribution two-dimensional mesh generating device is used for generating an earthquake source surrounding population distribution two-dimensional mesh. The earthquake radius area population calculating device is used for dividing the earthquake source surrounding population distribution two-dimensional mesh into n areas and respectively calculating gross population in each of the n areas. The first earthquake damage assessment device is used for assessing the first casualty number P in the area according to the earthquake information and distance between the earthquake source and each of the n areas divided by the earthquake radius area population calculating device. Therefore, the problem of serious time delay in data publishing caused by the fact that casualty numbers in earthquake disasters can be assessed only after acquiring multiple aspects of related information in related technologies is solved, and the technical effect of timely assessing casualty numbers in earthquake areas is achieved.

The invention relates to an MCNN crowd counting method based on global density characteristics. The crowd counting method can adapt to different density scenes, is capable of generating the crowd density map containing more comprehensive information, obtains the estimated number of people by integrating the crowd density map, and can avoid the influence of crowd occlusion and non-uniform crowd distribution in the crowd image on counting; and the convolutional neural network combined with global density characteristics is utilized to estimate the population density map, so that the method can be suitable for a non-uniform population distribution scene, the density map containing more comprehensive information is generated, and the population estimation result has higher accuracy and robustness.

A memory using techniques to extract the data content of its storage elements, when the distribution of stored states is degraded, is presented. If the distribution of stored states has degraded, secondary evaluations of the memory cells are performed using modified read conditions. Based upon the results of these supplemental evaluations, the memory device determines the read conditions at which to best decide the data stored.

The invention relates to a personalized movie recommendation method based on a self-adaptation orthogonal crossover multi-target optimization algorithm. The personalized movie recommendation method has the advantages that aiming at the optimization insufficiency of a traditional recommendation algorithm to accuracy and non-accuracy which are two opposite indexes, the multi-target optimization algorithm is used to optimize the two targets, and accordingly diversity is increased under the premise that the accuracy is kept; aiming at the defects of the NSGA-II multi-target optimization algorithm,the improved algorithm SMOCDE is provided, the algorithm designs a self-adaptation multi-target orthogonal crossover operator SMOC, and the operator is used to initialize population so as to avoid population distribution unevenness; the operator is used for perform crossover operation, and the convergence performance and distribution performance of population are kept; when the algorithm is applied to the actual problem of personalized movie recommendation, the universality and effectiveness of the algorithm are verified by testing and comparing the algorithm with an existing recommendation algorithm, and the accuracy and diversity of recommendation results are increased.

The invention provides a comentropy-based improved evolutionary multi-objective optimization method. Based on a comentropy-based improved Pareto envelope-based selection algorithm (PESA), comentropy measurement indexes are introduced into the PESA, and the evolutionary degrees of individuals in a population are considered in time after the PESA is evolved by utilizing the distribution characteristics of the comentropy indexes in a quantitative measurement Pareto solution set, such as the outstanding behaviors in the aspects of uniformity, diversity and convergence. After the population is evolved to be uniform and varied in population distribution, a comentropy numerical value is kept constant at the moment. According to the characteristic, when the solved comentropy numerical value is stably constant within a certain range, an optimization effect of the PESA is achieved, and hereon, an optimization computational process can be ended. Consequently, the complicated evolutionary process is avoided, and the time complexity is reduced.