216 results about "Vector method" patented technology

Methods for performing packet classification via prefix pair bit vectors. Unique prefix pairs in an access control list (ACL) are identified, with each prefix pair comprising a unique combination of a source prefix and a destination prefix. Corresponding prefix pair bit vectors (PPBVs) are defined for each unique source prefix and unique destination prefix in the ACL, with each PPBV including a string of bits and each bit position in the string associated with a corresponding prefix pair. A list of transport field value combinations are associated with each prefix pair based on corresponding entries in the ACL. During packet-processing operations, PPBV lookups are made using the source and destination prefix header values, and the PPBVs are logically ANDed to identify applicable prefix pairs. A search is then performed on transport field value combinations corresponding to the prefix pairs and the packet header to identify a highest priority rule.

Methods of delivering viral vectors, particularly recombinant AAV virions, to the CNS are provided. Also provided are methods of treating Parkinson's Disease.

The invention relates to an abnormal cardioelectric recognize method based on ultra-complete character. Wherein. it first uses secondary sample wavelet to process R-point check on the cardioelectric data, segments and pretreats the cardioelectric data based on the R-point position; then uses independent component method and disperse wavelet convert method to extract one ultra-complete character group from each heart beat, and contract the character via communication method; at last uses the vector method to train the extracted character to obtain one vector mode, to automatically recognize and classify the new heart beat data.

The invention discloses a convolutional neutral network-based attribute extraction method. The method comprises the following steps of (1) constructing an external knowledge library; (2) obtaining text data; (3) obtaining attribute-containing sentences by using a remote supervision method; (4) obtaining the sentences by utilizing a word vector method and performing vectorization; and (5) inputting the sentences to a convolutional neutral network, and performing training and classification. According to the method, the attribute-containing candidate sentences are extracted from a non-structured text data set based on artificially defined mapping by utilizing the external knowledge library in combination with remote supervision and convolutional neutral network models, and sentence classifications are classified in combination with the convolutional neutral network model, so that an attribute extraction task is finished.

An IGBT (insulated-gate bipolar transistor) stuck-open fault diagnosis method for a three-phase inverter bridge of a frequency converter belongs to the technical field of IGBT fault diagnosis of three-phase inverter bridges and solves problem that misdiagnosis is caused in instant load or instant unload when stuck-open fault of IGBTs of an inverter bridge is determined through the Park vector method. The method comprises the following steps: firstly, three phase currents ia, ib and ic of a motor stator used for speed regulation and controlled by the frequency converter are collected; secondly, the three phase currents ia, ib and ic are transformed through discrete Fourier transform to obtain cosine component aj, k and sine component bj, k of direct current component amplitude and fundamental component amplitude of the three phase currents ia, ib and ic; thirdly, the direct current component amplitude is normalized through the fundamental component amplitude to obtain a direct current component normalization value hj; fourthly, a fault phrase of the three-phase inverter bridge is determined according to the direct current component normalization value hj; and then whether the faultphrase is in an upper transistor or a lower transistor is determined. The invention is suitable for fault diagnosis of three-phase inverter bridges.

Disclosed is an improved technique for training a support vector machine using a distributed architecture. A training data set is divided into subsets, and the subsets are optimized in a first level of optimizations, with each optimization generating a support vector set. The support vector sets output from the first level optimizations are then combined and used as input to a second level of optimizations. This hierarchical processing continues for multiple levels, with the output of each prior level being fed into the next level of optimizations. In order to guarantee a global optimal solution, a final set of support vectors from a final level of optimization processing may be fed back into the first level of the optimization cascade so that the results may be processed along with each of the training data subsets. This feedback may continue in multiple iterations until the same final support vector set is generated during two sequential iterations through the cascade, thereby guaranteeing that the solution has converged to the global optimal solution. In various embodiments, various combinations of inputs may be used by the various optimizations. The individual optimizations may be processed in parallel.

The invention provides a human face depth and surface normal vector predication method based on a dilated convolution neural network. The method includes steps of training the dilated convolution neural network S1, constructing the dilated convolution neural network including a plurality of convolution layers, a plurality of dilated convolution layers and a plurality of deconvolution layers that are connected in sequence, wherein each convolution layer is connected with a normalized operation and an motivation operation; S2, initializing the weight value of the dilated convolution neural network; S3, inputting training pictures into the dilated convolution neural network and performing iteration training on the dilated convolution neural network targeting at minimizing a cost function andupdating the weight value after each iteration process; S4, inputting testing pictures into the dilated convolution neural network obtained through training and outputting a corresponding human face depth map and a surface normal vector map; S5, judging whether the predication precision of the dilated convolution neural network obtained through training meets requirements or not according to the output human face depth map and the human face normal vector method, ending the training if the precision meets the requirements, and returning to S3 for training again if the precision does not meetsthe requirements.

A reconfigurable processor including a plurality of reconfigurable slots, a memory, an instruction queue, a configuration selection unit, and a configuration loader. The plurality of reconfigurable slots are capable of forming reconfigurable execution units. The memory stores a plurality of steering vector processing hardware configurations for configuring the reconfigurable execution units. The instruction queue stores a plurality of instructions to be executed by at least one of the reconfigurable execution units. The configuration selection unit analyzes the dependency of instructions stored in the instruction queue to determine an error metric value for each of the steering vector processing hardware configurations indicative of an ability of a reconfigurable slot configured with the steering vector processing hardware configuration to execute the instructions in the instruction queue, and chooses one of the steering vector processing hardware configurations based upon the error metric values. The configuration loader determines whether one or more of the reconfigurable slots are available and reconfigures at least one of the reconfigurable slots with at least a part of the chosen steering vector processing hardware configuration responsive to at least one of the reconfigurable slots being available.

The invention discloses an intelligent commodity recommending method based on word vector data driving. The method includes the following steps: data pre-processing, word vector generation, score prediction constructing, model training and score prediction. The method includes the following steps: using a word vector method to take user numbers, commodity numbers and commodity scores as semantic words, changing the semantic words to a sparse vector by conducting one-hot encoding, then multiplying the sparse vector and a weight matrix and mapping a high-dimension and sparse original vector to a dense, continuous and fixed and low-dimension feature space, then inputting the original vector to a deep model for carrying out training to obtain weight parameters of each layer of a model, predicting and scoring a new user's favor to the commodity by using a well-used model so as to complete intelligent recommendation of the commodity to the user. According to the invention, the method applies the word vector method which classifies texts to score prediction and commodity recommendation which is based on favor of the user of an e-commerce platform to the commodity. The method can ensures precision and provide better explanation.

The invention discloses a word vector model based on point mutual information and a text classification method based on CNN. The method comprises the following steps: (1) training a word vector modelthrough a global word vector method based on point mutual information; (2) determining a word vector matrix of the text according to the trained word vector model; (3) extracting features from word vector matrix by CNN and training classification model; (4) extracting input text features according to the trained word vector model and CNN feature extraction model; (5) according to the text featuresextracted from CNN feature extraction model, calculating the mapping distance between text and preset categories by softmax and the cross entropy method, wherein the nearest one is the correspondingcategory of text. This method overcomes the shortcomings of Glove word vector in the semantic capture and statistical co-occurrence matrix, reduces the training complexity of the model, can accuratelymine the text classification features, is suitable for text classification in various fields, and has great practical value.

The invention discloses a court similar case recommendation model based on word vectors and word frequencies, namely a TF-W2V similarity calculation model. The judgment documents are divided into fivecase types of criminal affairs, civil affairs, execution, compensation and administrative affairs, and in order to process, store and query the judgment documents, the model extracts the key information from the submitted judgment, and finds out the judgment with the highest similarity in the same type of judgment in the document data by adopting a Word2Vc + TF-IDF text similarity algorithm to give out the similarity and recommend the judgment. According to the method, based on a word frequency and word vector method, the keywords and the word meaning information of the texts are integrated,and the similarity of the two texts is accurately calculated. The method is applied to the court judgment for similarity calculation, and the experimental results prove that the method is simple to apply, has no requirement for a labeling training set, can be applied to the texts in different fields, consumes the moderate time in calculation, is more accurate in obtained result compared with a traditional method, is closer to the expert evaluation results, and can calculate the similarity of the court texts accurately and effectively.

A network weight distance vector positioning method of radio transducer includes carrying out weight treatment on average each jump distance of each received anchor node based on DV-hop positioning means and considering estimated average each jump distance of multiple anchor nodes, calculating distance between unknown node and anchor node by utilizing final average each jump distance for effectively decreasing estimation deviation and for effectively raising the positioning accuracy of complete network.

The invention discloses a video description method based on multi-feature fusion. The method is characterized in that (1) deep spatial-temporal features of a video are extracted by fusing traditional CNN features and SIFT flow features; (2) an S2VT sentence generation model with added average pooling features being video overall features is adopted to generate corresponding sentence description according to the deep spatial-temporal features extracted in the step (1); and (3) a word2vec word vector method is adopted to replace one-hot vector word representation to optimize the sentence generation model in the step (2). The method has the advantages that more robust spatial-temporal features can be better extracted through multi-feature fusion; meanwhile, the average pooling features are added into the sentence generation model, so that more relations are established between visual information and words; finally, the word2vec word vector method is adopted to replace one-hot vector word representation, so that more relations are established between words, and video description performance is effectively improved.

The invention discloses a method for detecting remote sensing image change based on non-parametric density estimation, which mainly solves the problem that the estimation to the statistic items which relevant to a change type and a non-change type in a differential chart in the prior art has error. The realizing process of the method is that inputting two remote sensing images with different time-phase, removing noise of each channel of each image, obtaining noise-removing images of the two time-phase, and constructing difference images through adopting the change time-vector method, gathering the difference images into change type and a non-change type through applying K-means clustering algorism, obtaining the initial sorting results, and estimating the statistic items relevant to the change type and the non-change type in differential images through adopting non-parameter density estimation, carrying out the self-adapting space restriction combining the variable weight markov random field model, and obtaining the final change detecting results. The experimentation shows that the invention can effectively keeps the structure information of the images, removes insulation noise, improves the change detection processing efficiency, and can be used for the fields of disaster surveillance, land utilization and agriculture investigation.

The invention discloses a running state monitoring and diagnosing method of a hydraulic turbine set. The method comprises friction fault monitoring steps that 1) friction detection signals, including a horizontal vibration signal, a vertical vibration signal and a water leading throw signal, of a top cover of the detected hydraulic turbine set are collected; 2) a friction characteristic vector is constructed according to the friction detection signals of the top cover; 3) a weighted vector method is carried out on different friction characteristic vectors to generate a weighed friction characteristic vector; and 4) vector distance between the weighted friction characteristic vector and a preset typical friction characteristic vector is calculated, and if the vector distance is lower than a given threshold, it is determined that a friction fault occurs in the detected hydraulic turbine set. The method can be used to detect and diagnose the friction fault occurred in the detected hydraulic turbine set accurately, functions of a state monitoring system of a hydraulic power plant are completed, more useful information is provided for remote analysis and diagnosis of experts, and occurred faults can be handled in a targeted manner.

Provided is a method for carrying out community detection on a heterogeneous social network on the basis of a clustering algorithm. The method comprises the steps that an adjacent matrix is built; the community structure is initialized; the local modularity is calculated; a set of mark numbers of communities participating in fusion is obtained; candidate fusion sets are obtained; differences of the modularity are calculated; whether the first modularity difference and the second modularity difference meet the fusion standard or not, if yes, the mark numbers of the communities participating in fusion and the mark numbers of candidate communities are unified, and if not, the step of calculation of the local modularity is executed again; a new community structure is recorded; if community merging does not exist in the current cycle, the optimal community structure is output. According to the method for carrying out community detection on the heterogeneous social network on the basis of the clustering algorithm, due to the fact that the clustering method, the similarity vector method and the local modularity method are adopted, the methods can be effectively applied to community detection of the heterogeneous social network, and accuracy of the detection result of the heterogeneous network community structure is improved.

The invention discloses a rapid autonomous all-sky map fixed star identification method which comprises the following steps: (1), generating a navigation star diagonal pitch table according to a visual field of a star sensor; (2), sequencing all observation stars according to energy; (3), constructing an observation star triangle by using a novel triangle selection method; (4), performing triangle matching identification on the observation star triangle based on an improved K vector method; (5), if a triangle matching identification result is unique, calculating a current gesture and carrying out projection verification, if the result is not unique, performing tetrahedron identification, if a tetrahedron identification result is unique, calculating a gesture and carrying out projection verification, and if the result is not unique, re-selecting observation stars to form a tetrahedron for performing matching identification; and (6), performing projection verification on the identified unique result according to the calculated gesture, and if the projection verification is passed, indicating that the identification is successful. The rapid autonomous all-sky map fixed star identification method has the advantages of saving storage space, reducing star map identification time and increasing identification accuracy, and is great in practical value.

The invention relates to a self-adaptation mean-shift target tracking method based on optical flow field estimation. Aiming to solve the problem that the tracking fails due to high motion speed of the target or obvious scale variation and shielding of the target during target tracking of the mean-drift shift algorithm, a light stream method is introduced on the basis of a traditional mean-shift vector method, feature points are searched on the target, the centre position and size of a window are modified and tracked based on the variation information of the feature point, and more accurate length and width of the window can be obtained through self-adaptation by a Bhattacharyya coefficient bisection method. The area of the object shielded by a stationary object can be observed through aberration analysis, and the object can be recaptured by using the Bhattacharyya coefficient.

The invention relates to a fault identification method for inter-turn short circuit of stator windings in doubly-fed motor at sea. The method comprises the following steps of: obtaining three-phase voltage and three-phase current of the stator of the doubly-fed motor whose fault has yet to be recognized; calculating the three-phase voltage and three-phase current of the stator through a modified Park's vector method to obtain the 2 times fundamental frequency component amplitude I2p caused by the inter-turn short circuit fault of the stator windings; and determining whether the 2 times fundamental frequency component amplitude I2p caused by the inter-turn short circuit fault of the stator windings reaches the fault pre-warning value or not. If so, the doubly-fed motor whose fault has yet to be recognized at sea is confirmed to have an inter-turn short circuit fault. Compared with the prior art, the identification method of the invention has robustness to the motor stator voltage imbalance and load variation, and the discrimination is both accurate and strong.

The invention discloses a method of continuum structure non-probabilistic reliability topological optimization under the mixed constraints of displacements and stresses. According to the method, first, a continuum structure non-probabilistic reliability topological optimization model with weight lowering as an optimization objective is established; then, the vertex combination method is utilized to obtain upper and lower bounds of displacements and stresses, and thus corresponding reliability indexes are obtained; next, the non-probabilistic reliability indexes are replaced by utilizing optimization characteristic displacements so as to improve the convergency of problems, and the sensitivity of the optimization characteristic displacements to design variables is solved by utilizing the adjoint vector method and the compound function derivation law; finally, the design variables are updated with the method of moving asymptotes, repeated iteration is conducted until corresponding convergency conditions are met, and the optimal design scheme meeting reliability constraints is obtained. During the optimization design process, the comprehensive influence of uncertainty on the continuum structure property is reasonably represented, weight lowering can be effectively achieved, and it is guaranteed that the design itself gives consideration to both safety and economy.

The invention relates to global satellite navigation system receiver autonomous integrity monitoring technology and discloses a parity vector method-based double-satellite failure recognition method, aiming at the problems of false positives and false negatives caused by fault deviation offsetting when the parity vector method is used for recognizing two fault satellites. According to the technical scheme, the parity vector method is used for recognizing one fault satellite; with the fault satellite as the basis, four fault-free satellites are found out, and the information of the fault-free satellites is used for roughly locating, so that the fault satellites can be recognized; the recognized fault satellites are removed, and then the position resolution is carried out again, so that the locating accuracy is improved; therefore, the problems of false positives or false negatives caused by fault deviation offsetting can be avoided. The method solves the problem of the fault deviation offsetting caused by parity vector residual error and realizes the detection and the recognition for a plurality of fault satellites. After the method is used for detecting and recognizing satellite failure, the locating accuracy is improved. The method is mainly used for monitoring the autonomous integrity of a global satellite navigation system receiver.

Methods and devices for calculating an estimate of a wind vector. A maneuver of a platform may be identified as a steady-straight motion maneuver, a partial turn maneuver, or a full turn maneuver. A preliminary estimate of a wind vector corresponding to a wind condition prevailing near the platform may be calculated by applying a maneuver-specific calculating method. A gain associated with the preliminary estimate of the wind vector may be calculated by applying a state-specific calculation method. A filtered estimate of the wind vector may be updated based, at least in part, on the preliminary estimate of the wind vector and the gain associated with the to preliminary estimate of the wind vector.

The present invention provides an adaptive user autonomous integrity monitoring method. The method comprises: converting the system time of different navigation systems into the same as that of the main system; converting the reference coordinates of different navigation systems to be consistent with the main coordinates; respectively estimating a satellite clock difference, a troposphere delay, an ionosphere delay, and a multipath delay; obtaining a PDOP (position dilution of precision ) value through observation data and satellite ephemeris data; when PDOP<3, using the positioning domain-based location solving method; when 3<PDOP<6, using the measurement domain-based location solving method; when 6<PDOP, determining that the system is not available; using the parity vector method for fault detection; and using the maximum likelihood estimation for fault identification. According to the method provided by the present invention, the exploitation and utilization of observation information can be maximized, and the influence of the gross error on the positioning result can be reduced, so that the performance of the autonomous integrity monitoring of a receiver can be enhanced.

The invention relates to text classification of natural language processing, and specifically relates to a class center vector text classification method based on dependency, word class and a semanticdictionary. To overcome the semantic defect of a feature selection algorithm based on statistics, the invention introduces the dependency, the semantic dictionary and the word class to optimize and cluster text features, provides an improved weight calculation formula, and further provides an improved class center vector text classification method. The text classification method of the inventionhas advantages of both high classification efficiency of a traditional class center vector method and high classification precision of a K-nearest neighbor algorithm, and can be widely used in variousclassification systems.

The invention relates to a cascaded conditional random field-based product name recognition method and device, and belongs to the technical field of internet data processing and analysis. According to the method, words are expressed by utilizing a word vector method; the semantic similarity of the words is measured through the similarity of vectors; the global context information is fused through a method for combining the word vectors and word clusters; and in allusion to the problem that the product names are complicated in structure and have nesting phenomenon, a cascaded conditional random field model is used for carry out product name recognition. Compared with the prior art, the method and device provided by the invention have the advantages that the problems that the context information is insufficient in the product name recognition and the product names have nesting phenomenon and are complicated in structure are effectively solved, the performance of recognizing the product manes which are complicated in structure is improved, and the product name recognition correctness and FI value are higher than that of the traditional method.

Methods, systems and computer programs for distributing a computing operation among a plurality of processes and for gathering results of the computing operation from the plurality of processes are described. An exemplary method includes the operations of pairing a plurality of processes such that each process has a maximum of one interaction partner, selecting half of the data located at a process, dividing the selected half of the data into a plurality of data segments, transmitting a first data segment resulting from the dividing operation form the process to the interaction partner of the process, receiving a second data segment at the process from the interaction partner, concurrently with the transferring and receiving operations, performing a computing operation on a third data segment previously received from a previous interaction partner and a fourth data segment from the data segments, and iterating over the transmitting, receiving and computing operations until all the data segments have been exchanged.

The invention discloses a tool deflection modeling method for a multi-axis machining system. The tool deflection modeling method includes establishing a new non-deformable cutting thickness model, and a cutting force prediction model of arc-tool variable-posture milling by the vector method; establishing a flexibility model of a machine transmission shaft by the equivalent column method and a comprehensive flexibility model of the machining system by force ellipsoid method and coordinate system transformation; finally utilizing the cutting force model in the process of variable-posture machining and the flexibility model at the tail end of the multi-axis machining system to obtain a tool deflection model. In the tool deflection modeling method, the non-deformable cutting thickness model of a new tool cutting blade and the comprehensive flexibility model of the multi-axis machining system are used to obtain a more accurate tool deflection change law during machining, so that the tool postures during multi-axis machining and machining parameters such as feed speed and spindle revolving speed are optimized, tool deflection is controlled, and quality of machined surfaces of workpiece is improved.

The invention relates to a comprehensive method for vector or direct torque control of a permanent magnetism type wind generator, and belongs to a comprehensive method which uses the vector control when the permanent magnetism type wind generator is at a stable operation state and uses the direct torque control when the permanent magnetism type wind generator is in a wind power generating transient state. The comprehensive method mainly solves the technical problems existing in the prior wind generator vector method that the transient swing is hard to control due to the sudden change of wind force. The technical proposal comprises the following steps: a) establishing a vector control mathematical model of the permanent magnetism type wind generator in the stable operation state, due to the control of component voltage and current on the d and q axes by pulse width modulation(PWM), controlling the AC exciting current frequency of the permanent magnetism type wind generator by a grid electrode of a bipolar triode switch of a corresponding isolated gate in an adjusting converter, and realizing the control of the permanent magnetism type wind generator in a stable wind state; and b) in the wind power generating transient process, using the permanent magnetism type wind generator controlled by the method of the direct torque control of the permanent magnetism type wind generator(WGDTC).

The invention discloses a speckle noise suppression method for polarized SAR (Synthetic Aperture Radar) data based on a non-local mean value fused with PCA (Polar Cap Absorption), which mainly solves the problems that speckle noises in a homogeneous region can not be well filtered and the edge detailed information can not be effectively maintained in the traditional polarized SAR filtering method. The implementation process of the method comprises the following steps of: (1) inputting a coherence matrix T of the polarized SAR data; (2) maintaining a bright target for the coherence matrix T; (3) solving a characteristic vector of span data by utilizing a PCA method; (4) filtering the non-local mean value for elements of the coherence matrix T, wherein a filtering weight value is obtained by calculating the characteristic vector of the span data; and (5) generating a pcolor by utilizing the filtered coherence matrix T through a Pauli vector method. Compared with the prior art, the speckle noise suppression method remarkably improves the capability of speckle noise suppression of the polarized SAR data, can effectively smoothen the homogeneous region and maintain the edge detailed information, and can be used for the pretreatment process of the polarized SAR data.