35 results about "Local adaptation" patented technology

The invention relates to a flight management system for manned or unmanned aircraft having to face an emergency situation such as hijacking of the aircraft, medical emergencies, situations of failures affecting the propulsion, pressurization or communication functions for example. It provides for a device and process for automatically or semi-automatically generating a flight plan compatible with international regulations and their national or local adaptations with possibilities of optimization according to navigation parameters.

The invention discloses a self-adaptive regularized super-resolution image reconstruction method which can keep marginal definition, which mainly solves the problem that the prior method has edge fog in reconstruction of a degraded image. The method comprises the following steps: an imaging model is constructed; on the basis of an unconstrained objective function constructed by a Lagrangian multiplier method, gradient is increased to approach a bound term; the objective function is expanded; L1 norm is adopted to measure a data approximation term; a self-adaptive bilateral total variation model which can carry out local adaptive control on the smoothing effect is utilized to construct a self-adaptive regular term; a gradient approximation term is added to be as constraint of gradient consistency; edge information is kept; the self-adaptive regular term and a gradient consistency bound term are introduced as constraint conditions; an expanded Lagrangian objective function is constructed and optimized; and an optimized unconstrained objective function is utilized to reconstruct an image, thereby obtaining a high-resolution image of which the edge is kept. The method can keep image edge clear, can inhibit noise and is suitable for restoration treatment on the degraded image.

The invention discloses a dictionary database-based adaptive image super-resolution reconstruction method in the field of image processing. The dictionary database-based adaptive image super-resolution reconstruction method comprises the steps of: adaptively selecting a matched dictionary from a dictionary database according to a characteristic vector of each low-resolution image block, if the matching fails, re-training to obtain a proper dictionary, updating the dictionary into the dictionary database, then carrying out super-resolution reconstruction on the blocks by using the dictionary to obtain image blocks with high resolution, and finally, recombining all blocks to obtain a high-resolution image. The dictionary database-based adaptive image super-resolution reconstruction method is test in a face image, results prove that the method is superior to a method using a single dictionary in term of the image reconstructing effect, training image blocks with higher matching degree can be screened out in a process of training a local adaptive dictionary; and since many matched image blocks exist, prior information of a training set is sufficient amd a reconstructing effect is greatly improved compared with that of the method using the single dictionary.

The invention discloses a protein complex identification method based on key protein and local adaptation. Based on the importance of the key protein to life activities of organisms and the topological property of a protein interaction network, the invention discloses the protein complex identification method (EPOF) based on key protein and locally adapted protein by using the key protein as a seed. The protein complex identification method not only can be applied to a non-weighted protein interaction network, but also can be applied to a weighted protein interaction network. The protein complex identification method can be used for recognizing the protein complex more accurately only according to the protein interaction information and the key protein information, and predicting a large quantity of protein complexes in one step, and solves the problems of high cost, high time consumption and the like of the chemical experiment method.

The invention relates to a multi-constraint service selection method and device based on global QoS decomposition, which is realized by establishing a single object optimization model with a number of constraints. The method comprises the steps that a corresponding dependency set and collision set are established for each candidate service according to the service dependency transmission characteristic; the global QoS constraint is decomposed into local QoS constraints corresponding to each service class; candidate services which do not satisfy the local QoS constraints under the service class are filtered; all filtered candidate services are checked, and the dependency set and collision set of the remaining candidate services are updated; an adaptive replacement method is used to carry out quality scale combination replacement in an unresolved state; the local fitness of each candidate service is calculated; and the candidate service with the greatest local adaptability in each service class is selected to form the final combination service. According to the invention, the complexity and running time are greatly optimized; the real-time demand of a user is satisfied; the scale of a candidate service space is narrowed; and the quality and performance of the network combination service are effectively ensured.

The invention provides a silicon wafer surface topography reconstruction method based on B-spline. The silicon wafer surface topography reconstruction method based on the B-spline comprises firstly fitting an input triangular mesh boundary, and then giving a boundary curve to perform surface fitting of an interpolation, and generating a first level B-spline surface; secondly, performing local adaptive refinement fitting on an error transfinite portion in an upper level B-spline surface and repeatedly performing local refinement fitting in this way until a given error is met; and finally, adding the multilevel B-spline surface with the offset to a base plane to obtain a fitting result. The silicon wafer surface topography reconstruction method based on the B-spline can simultaneously meet overall fitting and local adaptation of surface reconstruction, and effectively avoids data redundancy; and is strong in robustness and has significant effects for solving non-linear problems and restraining fuzzy noises; and the reconstructed surface is smooth and burr-free, so that the method can better meet response frequency requirements of actual motion of a motor.

The present disclosure relates to an automated hairstyle processing method, an an automated hairstyle processing system, and an automated hair cutting device, the system (10) comprising a memory unit (48) arranged to store a predefined hairstyle model (90) including hair property representing values and body shape representing values, particularly head shape representing values, a sampling unit (76) arranged to sample an actual body shape portion, particularly an actual head shape, of a to be treated subject (12), involving detecting deviations from a model body shape portion of the predefined hairstyle model, a processing unit (42) arranged to adapt a hair property model (94) of the predefined hairstyle model, in case deviations between the actual body shape and the model body shape portion are detected, so as to compensate the actual body shape deviations, the adaptions to the hair property model (94) being local adaptations.

The present invention is suitable for the computer vision and mode identification technology field, and provides a method and device for single sample face identification based on local convolution feature combination representation. The method comprises: extracting corresponding image blocks to be identified at feature points with preset quantity of a face image to be identified in a feature point division mode; putting each identification image to be identified into a local adaptation convolution network corresponding to each feature point to extract the features of each feature point; calculating combination representation of the features of all the feature points on the face image to be identified according to a changing dictionary and a query dictionary in a category, and calculating the representation coefficient of the features of all the feature points in the combination representation according to a projection matrix and a temporary matrix; and according to the representation coefficient, the changing dictionary in the category and the features of each feature point on the face image to be identified, determining the identity of the face image to be identified so as to effectively improve the face identification robustness, reduce the face identification time consumption and effectively improve the face identification efficiency and identification accuracy.

The invention discloses a forest tree microscopic carbon sink metering assimilation box parameter measuring instrument. The measuring instrument is divided into two parts, namely, a host part and a slave part; the host part mainly comprises a power supply module, a single-chip microcomputer processing module, a TFT touch screen display module, a communication interface module and so on; the slave part includes a power supply module, a single-chip microcomputer processing module, a communication interface module, a tree diameter growth measurement module, a temperature and humidity measurement interface module, an illumination intensity measurement module, a CO2 concentration measurement interface module, a data storage module and so on. With the forest tree microscopic carbon sink metering assimilation box parameter measuring instrument of the invention adopted, multi-parameter measurement can be realized. The forest tree microscopic carbon sink metering assimilation box parameter measuring instrument has the advantages of high automation degree, high local adaptation, convenience in measuring place replacement and the like.

The embodiment of the invention provides an information acquisition system and method enabling field detection equipment to be flexibly connected. When the detection equipment is connected to data acquisition management equipment, self-described data packets are sent, automatic adaptation is carried out after the detection equipment is connected to the data acquisition management equipment, and itis achieved that the detection equipment with different varieties is dynamically connected to the same data acquisition management equipment. Secret data transmission is carried out without the Internet processing technology, the functions such as local registration, local adaptation and local analysis under the local area data transmission network condition are achieved, secret data informationleakage caused by universal means such as network side adaptation and network data processing is avoided, the actual problems such as corresponding equipment adaptation and data access can be solved in field detection of the portable heavy metal detection equipment, and a series of operations such as local registration, adaptation and data frame analysis of the detection equipment are completed under the condition that the Internet is not available.

The invention provides a method for estimating successive difference evolution of a medium quality factor. The method comprises the steps of 1, preprocessing an actual VSP channel set to remove non-consistency of different wavelets; 2, creating an initial group X containing M units; 3, dividing an underground medium into different stratums according to the distance of two adjacent wave detectors, treating each channel as a sub-component, and defining local adaption function of the k unit through L1 norm so as to obtain the local adaption value of each channel; 4, optimizing and updating each sub-component in the initial group X according to the actual VSP channel and the local adaption of each channel; solving the quality factor and speed to be estimated after variation according to the variation strategy; 5, repeating step 4, updating the whole group, and selecting the optimal unit according to the global adaption value so as to obtain the quality factor and speed of underground medium. The method is fast to calculate, high in convergence, and free of dependence on the initial value.

A method for controlling optimization through self-organized extremum optimization process is used for solving continuous function optimization through the self-organized extremum optimization process, and the solved optimum solution is used as control parameters. The self-organized extremum optimization process comprises the following steps: step 1, determining the local fitness function of a continuous function f(x) according to a control objective; step 2, generating the initial solution s of the continuous function f(x) at random and setting the current optimum solution sbest; step 3, working out the local fitness of each individual si (i equals to 1, ..., m) aiming at the current initial solution; step 4, finding out the local fitness with the maximum fitness value; step 5, structurally changing the neighbourhood space N(s) of the individual sj corresponding to the local fitness with the maximum fitness value, selecting a neighbourhood solution s' belonging to N(s), and taking the selected neighbourhood solution as a new solution unconditionally; step 6, substituting the new solution and the current optimum solution sbest into the continuous function respectively so as to judge that whether the formula that f(s') is less than or equal to f(sbest) is workable or not, if the formula that f(s') is less than or equal to f(sbest) is workable, setting the new solution to be the final optimum solution, and if the formula that f(s') is less than or equal to f(sbest) is not workable, returning to step 2.

The invention discloses an extremum optimization method for solving the maximum satisfiability problem. The method defines local fitness and global fitness functions that meet the requirements of the extremum optimization method according to the principle of decomposability and linearity; uses Bose-Einstein probability distribution to construct optimization The initial solution; when selecting poor local variables for mutation, multiple probability distributions such as exponential distribution or mixed distribution are used as the extended evolution probability distribution. Compared with the traditional optimization method, the optimization process of this method is more instructive, and it is easier to obtain a near-optimal solution or even a global optimal solution. Its principle is simple and clear, with fewer adjustment parameters, simple design, and easy implementation.