34results about How to "Fast and efficient identification" patented technology

A method for detecting an information item within an information sequence obtained from a digital medium, said information item comprising any one of a specified set of prestored information items, comprising: transforming each of the set of prestored information items into a respective representation, in accordance with a predetermined transformation format; transforming the information sequence obtained from the digital medium, in accordance with the transformation format; and determining the presence of one or more of the prestored information items within the transformed information sequence, utilizing the respective representation, wherein the information items are divided into sets, and applying a security policy that depends on the number of detected information items that belong to the same set.

The invention discloses a method for identifying maize haploids. The method for identifying maize haploids provided by the invention comprises the following steps: (1) taking whether each corn kernel in a training set is a real haploid or a real diploid as a dependent variable, taking a near-infrared transmittance spectroscopy absorption value of each corn kernel as an independent variable, and establishing a haploid identification model by using a machine learning algorithm, wherein the training set is composed of a plurality of corn kernels, one part of the corn kernels are the real haploids, and the other part of the corn kernels are the real diploids; and (2) taking to-be-detected corn kernels, acquiring the near-infrared transmittance spectroscopy absorption values, inputting the absorption values into the haploid identification model established in the step (1), and outputting the result that the to-be-detected corn kernels are predicted haploids or predicted diploids by the model. The method provided by the invention can be used for automatic identification and has significances for promoting the maize haploid breeding technique engineering.

The invention discloses a multilevel semantic feature-based face feature extraction method and recognition method. The method includes the following steps that: 1) organ areas of each image in a facial image set A are divided; 2) bottom-level features of each organ are extracted and clustered; two clusters are extracted from clustering results and are adopted as positive and negative samples, and the positive and negative samples are trained in a paired combination manner such that a classifier set can be obtained, and the results of discrimination which is performed on the bottom-level features by the classifier set are united so as to obtain the middle-level features of the organ; the images in the A are the classified according to tags; any two classifications are selected from classification results of the tags and are adopted as positive and negative samples, and the positive and negative samples are trained in a paired combination manner such that a classifier set can be obtained, and the results of classification and discrimination which are performed on all the middle-level features in the A by the classifier set are united so as to obtain high-level features of the tags; the bottom-level features, the middle-level features and the high-level features are adopted to construct face features of the images; face features Vq are generated for any image q to be searched; and the face features Vq are matched with the face features in the A, and query results are returned. With the multilevel semantic feature-based face feature recognition method and recognition method adopted, recognition accuracy and stability can be improved.

Disclosed is a chemical metrology discrimination method for plastic type which comprises: employing optical spectrum information of plastic infrared spectrum characteristic area, selecting characteristic area infrared spectrum as input variable of competitive back propagation artificial nerve network with nonlinear iterative least square variable selecting method; the competitive back propagation artificial nerve network comprises front end back propagation nerve network and back end competitive function layer, taking unit matrix as network training target, wherein the unit matrix having the amount of known plastic resin sample as rank, having training on network connection weight value with known type plastic sample input matrix and analog noise input matrix and establishing network connection; input variable being converted by back propagation network layer and competitive function layer and the most significant neuron being shown as result, therefore plastic type discrimination system being established. The inventive method has the advantages of being rapid and accurate.

The invention discloses a method for quickly identifying multi-channel power signal patterns based on the SSA algorithm. By acquiring the low-frequency oscillation signal of the multi-channel power system, the signals are separated by an independent component method, so that the multi-channel time-varying signals are separated into mutually independent Statistical independent components, and then use the SSA algorithm to construct the leader and follower individuals, select the parameters of the low-frequency oscillation object as the leader and follower individuals, initialize the individual, calculate the fitness of each individual, and use the adaptive weight to carry out Iteratively update to find the optimal fitness value of the individual, so as to identify the low-frequency oscillation mode. The fast identification method of the present invention can well separate the multi-channel low-frequency oscillation signals, so that the system can quickly and effectively identify the low-frequency oscillation mode, and process the power data in real time.

The invention discloses a maize variety identification method based on near infrared spectrum and information processing. The method comprises the following steps of: collecting spectrum data of maize seeds by adopting a Fourier transform diffuse reflectance near-infrared spectrometer; analyzing main components by adopting normalization according to the characteristic of spectrum, i.e. normalizing the quadratic sum of projections of sample points on each principal axis; adjusting the distribution of the sample points in a feature space; adjusting the weights of the main components according to the diffusion condition of the data projections on each principal axis; and finally, classifying by adopting a nearest neighbor classification method. Compared with the traditional chemical identification method, the method provided by the invention is efficient and rapid and needs no professional person to operate.