68 results about "Relevance prediction" patented technology

A pharmaceutical dosage form such as a capsule capable of delivering therapeutic agents into the body in a time-controlled or position-controlled pulsatile release fashion, is composed of a multitude of multicoated particulates (beads, pellets, granules, etc.) made of one or more populations of beads. Each of these beads except an immediate release bead has at least two coated membrane barriers. One of the membrane barriers is composed of an enteric polymer while the second membrane barrier is composed of a mixture of water insoluble polymer and an enteric polymer. The composition and the thickness of the polymeric membrane barriers determine the lag time and duration of drug release from each of the bead populations. Optionally, an organic acid containing intermediate membrane may be applied for further modifying the lag time and / or the duration of drug release. The pulsatile delivery may comprise one or more pulses to provide a plasma concentration-time profile for a therapeutic agent, predicted based on both its pharmaco-kinetic and pharmaco-dynamic considerations and in vitro / in vivo correlations.

Enhanced meteorological measure-correlate-predict systems and methods. The systems and methods preferably consider publicly available, long-term data sets at each of a plurality of locations nearby a potential wind farm location. A test tower is preferably located at the potential location to collect a shorter-term data set, which, in combination with the long-term data set, is used to correlate and train embodiments of the systems and methods of the present invention using computational learning systems. Longer-term data can then be predicted for the potential wind farm location based on the correlation.

Enhanced meteorological measure-correlate-predict systems and methods. The systems and methods preferably consider publicly available, long-term data sets at each of a plurality of locations nearby a potential wind farm location. A test tower is preferably located at the potential location to collect a shorter-term data set, which, in combination with the long-term data set, is used to correlate and train embodiments of the systems and methods of the present invention using computational learning systems. Longer-term data can then be predicted for the potential wind farm location based on the correlation.

A correlation between develop inspect (DI) and final inspect (FI) profile parameters are established empirically with test wafers. During production, a wafer is measured at DI phase to obtain DI profile parameters and FI phase profile parameters are predicted according to the DI profile parameters and the established correlation. Each wafer is subsequently measured at FI phase to obtain actual FI profile parameters and the correlation is updated with actual DI and FI profile parameters.

An encoder (1600) and decoder (1700) for improving bit rate efficiency in a wavelet based codec includes an analysis filter bank (1601) for decorrelating the input data signal. A set of decimators (1701) are used to down sample the filtered input data signal and a predictor (1705) is used to extract cross subband dependence. The predictors (804, 904, 1104, 1204, 1304) are used in order to reduce the number of bytes of an encoded input data signal X(Z). The predictors exploit existing correlation amongst the subbands resulting from a multi-level analysis wavelet transformation or filter bank processing. Decimation required by the analysis filter bank is placed around the predictor on the basis of spatial location variance minimization to further facilitate subband prediction, and on computational complexity of the overall system.

The invention provides a method for measuring collapse influence factors of a punched bored concrete pile hole wall. The method for measuring collapse influence factors of the punched bored concrete pile hole wall comprises the following steps that collapse hole testing study area is confirmed; punched hole geometry factors of the collapse hole testing study area are collected and a theory volume Vi and an actual volume ( please refer to the formula )of a punched hole are calculated; qualitative factors and quantitative factors of the punched hole are confirmed and the qualitative factors and the quantitative factors are respectively defined; a collapse response matrix is confirmed; a collapse rate of the drill hole and a collapse hole reference variable are confirmed; a correlation evaluation formula of collapse hole collapse factors is established; a precision evaluation is conducted on the correlation evaluation formula of the collapse hole collapse factors; an analysis and an evaluation are conducted on an evaluation precision of a correlation prediction formula of the collapse hole collapse factors by applying a complex phase correlation figure of the correlation evaluation formula; an effect degree and size of the collapse factors of all collapse holes are analyzed and evaluated, and major collapse factors and minor collapse factors of the collapse holes are confirmed. According to the method for measuring collapse influence factors of the punched bored concrete pile hole wall, due to the fact that the qualitative variables are divided based on the values, the important practical value towards the analysis and evaluation of influence factors of a hole wall of a bored concrete pile collapse hole is achieved.

The invention discloses a parallel drug-target correlation prediction method based on sorting learning, and belongs to the field of bioinformatics. According to the method, multiple types of similarity, correlation characteristics, chemical spatial characteristics and gene spatial characteristics are extracted through multiple characteristic extraction methods; then, due to the fact that a characteristic set with high dimensionality can be obtained through multi-angle characteristic extraction and samples do not have conventional positive and negative example labels, dimensionality reduction processing is conducted through a principal component analysis method, then, the dimensionality reduced characteristic set is input into a sorting learning algorithm, and finally the drug-target correlation degree under each query can be predicted and output. By utilizing sorting learning, the drug-target correlation is no longer simply divided into correlation or uncorrelation, and sorting is performed according to the correlation degree of the drug and the target, so that research and development of a new drug are facilitated, and redirection of the drug is also facilitated.

The embodiment of the invention provides a synonym recognition model training method, a synonym determination method and synonym determination equipment, and relates to the technical field of machinelearning and computers. The method comprises the steps of obtaining a plurality of words; obtaining multi-source feature information of the words, wherein the multi-source feature information comprises semantic feature information and character feature information; determining a plurality of training samples based on the plurality of words; determining a synonym prediction result and a correlationprediction result of the training sample based on the multi-source feature information of the two words in the training sample through a synonym recognition model, the correlation prediction result being a prediction result of correlation between the two words in the training sample; calculating a loss function value of the synonym recognition model based on the synonym prediction result and thecorrelation prediction result of the training sample; and training the synonym recognition model according to the loss function value. According to the technical scheme provided by the embodiment of the invention, the synonym identification accuracy can be improved.

The invention discloses a 3D target tracking method and system based on point cloud sequence data, and belongs to the field of digital image recognition. The 3D target tracking method comprises the following steps: respectively extracting and standardizing a search point cloud and a template point cloud containing a target frame from a current frame and a previous frame, and predicting the position and posture of the target frame in the current frame by utilizing a 3D target tracking model so as to determine the position of a 3D target in the current frame, wherein in the 3D target tracking model, the feature extraction network is used for extracting template point cloud features and searching point cloud features, and the correlation prediction network is used for predicting a target score of each feature point in the search point cloud, and the integrated regression network is used for carrying out point-by-point regression after the two features are fused, and the position prediction network is used for carrying out weighted multiplication on the distance and posture between each feature point in the fused features and the center of a target box according to the target score ofeach feature point of the search point cloud. According to the 3D target tracking method, the three-dimensional attribute of the object can be fully utilized, and the calculation efficiency and stability of 3D target tracking are improved.

The invention discloses a convolutional neural network-based bug positioning method combining source code semantics and grammatical features. The method is characterized by providing the method for positioning a source code file for generating a bug according to a bug report submitted by a user; according to the method, a CNN is used for carrying out feature extraction on a bug report, source codesemantics and source code syntax, then the features are fused, unified features are extracted, finally, the CNN is used for carrying out correlation prediction on the bug report and source codes, andTopK source code files related to the bug report are obtained. Therefore, when a user submits a new bug report, a maintainer can perform positioning of the related source code file in time and notifya developer of repairing, and the bug repairing efficiency and the project maintenance efficiency are improved. The whole process of the method is shown in the attached drawing of the abstract.

Managing resources in a contact center including assigning each resource to one of a first set of resources each comprising a proficiency level above a first threshold for a first resource attribute, or a second set of resources each comprising a proficiency level below the first threshold for the first resource attribute and a proficiency level above a second threshold for a second resource attribute. An expected number of contacts requiring resources possessing one of the first or second resource attribute is predicted for a time period, and a correlation between the first and second resource attribute is identified. Based on the correlation, a minimum number of resources from each set required to handle the expected number of contacts at a predetermined service level for the time period is forecasted. The minimum number of resources from the first set is less than a number of resources required without the correlation.

The invention provides a method for increasing credibility of question prerequisite in a visual question answering scene. The method comprises following steps: prerequisite information extraction, question correlation prediction database, question correlation detection, visual question answering data expansion. First, the prerequisite information in the problem is extracted; the problem correlation prediction and explanation database are constructed; and binary classification of problem image pairs (Ii, Qi) is performed; whether the image Ii has the prerequisite information in question Qi is identified; then on the basis of one-hot coding, the image Ii and the problem Qi are encoded using the VGG network and the short and long term memory network, respectively, and input the data to the multi-layer sensor for prediction. The method of the invention can handle a plurality of target objects and their relations in different scenes, provide an encoding method to calculate the image matching distance, and improve the credibility of the question prerequisite information.

The embodiment of the invention discloses a public opinion analysis method and device, electronic equipment and a readable storage medium, and relates to the technical field of big data. The specificimplementation scheme is as follows: receiving a public opinion analysis request from terminal equipment, wherein the public opinion analysis request comprises an analysis keyword set by a user; and obtaining at least one document related to the analysis keyword according to the correlation result of the analysis keyword and each document in a target document set, the correlation result being obtained by a correlation prediction model through prediction by using correlation features, and the correlation features being obtained in advance based on the analysis keyword and each document; performing public opinion analysis on the at least one document to obtain a public opinion analysis result for the analysis keyword; and sending the public opinion analysis result for the analysis keyword tothe terminal equipment. According to the method, the accuracy and efficiency of correlation calculation can be ensured, and the actual public opinion analysis requirement is met.

The invention discloses an RFID spatio-temporal data traffic flow characteristic parameter prediction method which comprises the following steps: S1, acquiring traffic data of an RFID acquisition target road section, and performing spatio-temporal correlation analysis on the traffic data; S2, obtaining correlation between traffic flow characteristic parameters influencing the traffic state of thetarget road section and traffic flow characteristic parameters capable of reflecting the traffic state of the target road section; S3, predicting traffic flow characteristic parameters of the target road section in a traffic flow stable state and a traffic flow unstable state; and S4, carrying out weighted combination on the traffic flow characteristic parameters in the two states. The method solves the problems of large calculation amount, poor real-time performance and anti-interference capability, low prediction precision, low prediction efficiency and the like of an existing prediction method, and has the advantages that accurate, comprehensive and reliable traffic flow characteristic parameter prediction can be realized, and a new thought is provided for improving the traffic jam problem.