38results about How to "Reduce callouts" patented technology

The invention discloses a Chinese noun screening method, system, device and medium based on edit distance, and relates to the field of text processing, including: constructing a data dictionary, wherein words in the data dictionary are stored in groups, and each phrase corresponds to A word leads and a plurality of similar words; Obtain benchmark word, described benchmark word is matched with the word lead in the data dictionary, if matching is successful, then obtain a plurality of similar words corresponding to the word lead; A similar word is combined with the reference word to obtain a screening phrase; Calculate the similarity between each word in the screening phrase and each word in the screening data set; Filter out the similarity greater than a threshold from the screening data set The corresponding words obtain the screening results; the present invention adopts a data dictionary to expand the scope of noun data screening and improve the accuracy of data screening.

The present invention relates to a method and device for locomotive change and hang operation based on the CTC3.0 system. The track state information realizes the function of locomotive detachment; according to the interlocking route information, design the operation arrangement conditions of the main machine entering and exiting the warehouse as the basis for planning, and use the planned train number information, the line information of the locomotive entering and exiting the warehouse, and the signal or track occupancy , excavate the internal correlation information between the lead locomotive and the train number, realize the calculation of the lead locomotive detachment operation plan, and complete the handling of each locomotive detachment route through the detachment operation plan. Compared with the prior art, the present invention solves the problem of unmanned preparation of the locomotive operation plan in the existing general-speed hub station, and realizes the advantages of safety card control when the locomotive unhooking operation route is handled.

The invention discloses a Tibetan speech corpus tagging method and system based on cooperative batch active learning, wherein the system includes: a sample selection module, a manual tagging module, a tagging decision module, a tagger evaluation module, and a training set generation module. The invention solves the construction of the sample evaluation function and the proof of the nature of the submodular function through the approach to the optimal batch sample selection method, and solves the construction of the labeling decision function, the construction of the labeler evaluation model and the labeler-assisted learning model through the labeling committee collaborative labeling method mold. In addition, the system of the present invention can realize functions such as optimal selection of samples, annotation evaluation of users, sharing of annotation information and Tibetan phonetic knowledge, assisted learning by annotators, and is intended to improve the tagging quality of Tibetan speech data and speed up the construction of audio corpus.

The invention relates to a transferable image recognition method and device, and relates to the technical field of image recognition. The method includes: determining an image type of an input image recognition model; The source domain image is passed through the feature extractor and class predictor, and the cross entropy loss is determined; when the input image is an unlabeled target domain image, the target domain image is passed through the feature extractor and the domain discriminator, while the feature extractor and Category predictor; determine the adversarial loss according to the output of the domain discriminator and the similarity between the target domain image and the center point of each source domain image; determine the information maximization loss according to the output of the category predictor; according to the cross entropy loss, confrontation Loss and Information Maximization Loss to Optimize Image Recognition Models. Through the technical solution, the performance of target image recognition can be effectively improved, the annotation for target image recognition can be effectively reduced, and manpower and material resources can be greatly reduced.

The present invention relates to the technical field of insect behavior recognition, and discloses a spatio-temporal context-based insect behavior recognition method, device, and computer storage medium, comprising the following steps: acquiring insect behavior videos, and acquiring artificially marked original images of insect behavior videos. image; extract the insect image from the original image where the target insect is located, and convert the original image into a grayscale image; generate a spatial image from the insect image through a time window, and generate a time image from the grayscale image through a time window; according to the spatial image and the time image to generate feature images, mark the insect behavior in the feature image, and classify according to the behavior; use the classified feature image as a training sample to train the neural network to obtain the insect behavior recognition model; use the insect behavior recognition model to test Recognition of insect behavior in video. The invention has the technical effects of high insect behavior recognition accuracy and simple acquisition of training data.

The invention discloses a manufacturing method for a rack of a marine low-speed diesel engine. The manufacturing method comprises the following steps that firstly, the rack is divided into a rack head segment module, a rack middle segment module and a rack tail segment module, wherein the rack middle segment module comprises a plurality of rack middle segments which are the same in structure and size; secondly, according to the design size of the rack, all part plates of all the rack segments are cut for blanking, then grooves are processed and welded, and finally the plates are stored in a classified mode; thirdly, a CO2 welding method is adopted for welding the rack head segment module, the rack middle segment module and the rack tail segment module step by step respectively; fourthly, the rack head segment module, the rack middle segment module and the rack tail segment module are jointed and folded; and fifthly, annealing treatment is finally conducted on connection weld joints to reduce residual stress produced by welding of connection areas, and after overall heat treatment on the rack is completed, aging treatment and flaw detection are conducted to make the rack reach all design standards. The manufacturing method has the advantages of being high in production efficiency and good in interchangeability and universality.

The invention discloses a model training method for cross-domain sentiment analysis based on a convolutional neural network, belongs to the field of cross-domain sentiment classification, and aims tosolve the problem of cross-domain sentiment analysis. The method comprises: S1, preprocessing a text; S2, training a word vector model; S3, performing cross-domain model migration; wherein the step S3is carried out in sequence; a neural network model is trained through a source domain; migrating the trained model. Firstly, a weight value of a convolution kernel in a model is shared, a convolutionkernel weight trained in a source domain is used for extracting corresponding features in a target domain, a small part of data in the target domain is trained again, parameters of a full connectionlayer weight of the previously trained model are adjusted, and the effect is that model migration is conducted on a cross-domain emotion text.