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209results about How to "Reduce computing consumption" patented technology

Distributed semantic and sentence meaning characteristic fusion-based character relation extraction method

The invention relates to a distributed semantic and sentence meaning characteristic fusion-based character relation extraction method, and belongs to the field of natural language processing. The method comprises the steps of firstly performing training in a small amount of marked corpora and a large amount of unmarked corpora by utilizing statistic word frequency features and a Bootstrapping algorithm to obtain a relational feature dictionary; secondly constructing a triple instance of a statement through an element distance optimization rule, and constructing a triple feature space by fusing distributed semantic information and semantic information; and finally performing true-false binary decision on a triple, and obtaining a character relation type by utilizing a confidence degree maximization rule. According to the method, automatic generation of the feature relation dictionary is realized; a conventional relational multi-class problem is converted into a triple true-false binary decision problem, so that a conventional machine learning classification algorithm is better adapted; and by utilizing the distributed semantic information, the accuracy of relational classification is improved.
Owner:BEIJING INSTITUTE OF TECHNOLOGYGY

Rapid identity authentication method based on C/S mode

InactiveCN103491094AReduce computing consumptionReduce network data transmission consumptionUser identity/authority verificationClient-sideEncryption
The invention provides a rapid identity authentication method based on a C/S mode. The rapid identity authentication method based on the C/S mode comprises the steps that (1) a client and a server generate respective random numbers in sequence, carry out encryption on the random numbers, send the encrypted random numbers to each other, carry out another encryption on decrypted random number data of the opposite side, and send the encrypted random number data back to each other, and in the process, the server carries out legitimacy authentication on identity information of the client, and encryption processing is carried out after the client passes through the authentication; (2) the client and the server carry out decryption on the returned data to obtain the random numbers which are processed through encryption protection transmission, comparison is carried out between the random numbers and the random numbers generated by the client, comparison is carried out between the random numbers and the random numbers generated by the server, and the client and the server pass through bidirectional identity authentication when the random numbers and the random numbers generated by the client and the server are consistent. According to the rapid identity authentication method based on the C/S mode, the legitimacy authentication processing process of the client to certificates of the server in the process of authentication is omitted, the process is simple, the process of authentication is rapidly optimized, safety can be ensured, and the rapid identity authentication method based on the C/S mode is especially applicable to the identity authentication processing process of a mobile terminal when the mobile terminal which is limited in terminal operation resource and network transmission capacity is connected to an application system.
Owner:成都三零瑞通移动通信有限公司

Optimization method and device of convolutional neural network (CNN) and computer storage medium

The embodiment of the invention discloses an optimization method and device of a convolutional neural network (CNN) and a computer storage medium. The method includes: constructing the convolutional neural network, wherein the convolutional neural network includes at least four network layers of an image input layer, at least one convolutional layer, at least one pooling layer and at least one fully connected layer; decreasing the number of convolution kernels in the CNN when the number of to-be-detected objects is less than a preset threshold value; dividing an input image of the image inputlayer into at least one memory data segment, which is stored by utilizing continuous memory, according to a set border determination strategy, and utilizing a set continuous memory copy function to carry out data copying on each memory data segment; and merging original parameters in a batch normalization (BN) layer and parameters of the convolutional layer or the fully connected layer according to the set merging strategy, and using merged parameters as new parameters of the batch normalization layer, wherein the batch normalization layer is after the convolutional layer or the fully connected layer. Calculation consumption in carrying out detection through the CNN is reduced.
Owner:MIDEA GRP CO LTD

Target following and dynamic obstacle avoidance control method for speed difference slip steering vehicle

The invention belongs to the technical field of unmanned driving, and discloses a target following and dynamic obstacle avoidance control method for a speed difference slip steering vehicle, and the method comprises the steps: building four neural networks through employing a depth determinacy strategy in reinforcement learning; constructing a cost range of the obstacle so as to determine a single-step reward function of the action; determining continuous action output through an actor-critic strategy, and updating network parameters continuously through gradient transmission; and training a network model for following and obstacle avoidance according to the current state. According to the method, the intelligence of vehicle following and obstacle avoidance is improved, and the method canbetter adapt to an unknown environment and well cope with other emergencies. the complexity of establishing a simulation environment in the reinforcement learning training process is reduced. By utilizing a neural network prediction model trained in advance, the position and posture of each step of the target vehicle and the obstacle can be obtained according to the initial position and posture ofthe target and the obstacle and the action value of each step, so that the simulation accuracy and efficiency are improved.
Owner:BEIJING INSTITUTE OF TECHNOLOGYGY

Hierarchical network structure search method, device and readable storage medium

The invention provides a hierarchical network structure search method, a device and a readable storage medium. The method comprises the steps of S1, constructing a super network; S2, acquiring image data and taking the image data as training data of network parameters and structure parameters of the super network; S3, generating a feature map, calculating a cross entropy loss function of the network parameters, and updating the network parameters of the super network; S4, generating a feature map and a KL divergence loss function, calculating a cross entropy loss function of the structure parameters to obtain a semi-implicit variational discard loss function, training and updating the structure parameters of the super network and obtaining a discard probability; s5, updating the basic unitby using the discard probability, and updating the annealing parameter; s3 to S5 are repeated, and the network parameters and the structure parameters are updated; s6, obtaining a final network. Themethod greatly reduces the search time and reduces the calculation complexity while guaranteeing the higher performance, guarantees the search stability and practicality, can be used in the fields ofimage target detection and classification, and improves the image processing speed.
Owner:SHANGHAI JIAO TONG UNIV

Graph neural network training method and device

The embodiment of the invention provides a graph neural network training method, wherein the method comprises the steps: obtaining a relation network graph, wherein each object node corresponds to a sampling probability set, and the sampling probability set comprises the sampling probability of each first-order neighbor node; performing multiple rounds of iterative updating on the graph neural network on the basis of the relation network graph, wherein any round comprises the steps of performing M-order neighbor node sampling by taking a first label node selected in the round as a center, wherein any i-order neighbor node sampling comprises any first node in a sampled i-1-order neighbor nodes; based on a current sampling probability set, sampling a plurality of neighbor nodes from a first-order neighbor node full set, and classifying the neighbor nodes into an i-order neighbor node; performing the current round of updating on the graph neural network based on the sampled neighbor nodes within the M order and the first service label carried by the first label node; and determining a plurality of training feedbacks corresponding to the plurality of neighbor nodes by using theupdated graph neural network, and then updating the current sampling probability set of the first node.
Owner:ALIPAY (HANGZHOU) INFORMATION TECH CO LTD

High-precision Chinese predicate identification method

The invention relates to a predicate identification method based on a combination of rules and statistics, and belongs to the field of natural language processing and machine learning. The identification method aims at achieving high-precision and high-efficiency predicate identification. The stepped identification method identifies predicates from morphology and syntax labeled sentences, and comprises the steps of conducting morphological analysis on the sentences to be detected, obtaining suspicious predicates and a number thereof, preliminarily identifying the predicates by using preliminary identification judgment conditions, extracting relevant morphological and syntactic characteristics of the suspicious predicates dissatisfying the preliminary identification judgment conditions, judging the predicates with a decision-making tree judgment model obtained from C4.5 training, and finally summarizing identification results in the two steps to present the predicates of the sentences to be detected. The identification method has the characteristics of high accuracy rate, identification speed and identification rate for the non-verbal predicates, and the like, is applicable to the field requiring high-precision Chinese predicate identification, greatly promotes development of sentence meaning analysis, and has high application and popularization values.
Owner:BEIJING INSTITUTE OF TECHNOLOGYGY

Multi-class Bagging gait recognition method based on multi-characteristic attribute

The invention relates to a multi-class Bagging gait recognition method based on a multi-gait characteristic attribute, which belongs to the technical field of pattern recognition. According to the method, a nearest neighbor classifier is used as a weak classifier, and an integration classifier is constructed by expanding a two-class attribute Bagging method to a plurality of classes on the basis of 20 gait attribute characteristic sets after wavelet packets are decomposed and principal components are completely analyzed so as to carry out gait identity identification. The method comprises the following steps of: preprocessing, extracting characteristics and finally classifying test samples by using a method combining a nearest neighbor classifying principle and an MCAB algorithm. According to the multi-class Bagging gait recognition method based on the multi-gait characteristic attribute, a method fusing wavelet packet decomposition (WPD) and (2D) 2 principal component analysis (PCA) is adopted for the first time to extract and also select gait characteristics. The problem of loss of high-frequency components in a traditional gait recognition method based on wavelet transformation or overlarge dimensionality caused by simply adopting all data is solved. The multi-class Bagging gait recognition method based on the multi-gait characteristic attribute has higher recognition rate and visual angle change robustness.
Owner:BEIJING UNIV OF TECH

High-spectral image sharpening method based on probability matrix decomposition

The invention discloses a high-spectral image sharpening method based on probability matrix decomposition, and belongs to the field of remote sensing image processing. The method is characterized in that the method comprises the steps: carrying out the preprocessing of two inputted images based on the hypothesis that a pixel spectrum vector of a high-resolution high-spectral image is just formed by the linear superposition of a few of vectors with the hidden spectrum features according to one low-resolution high-spectral image and one high-resolution high-spectral image and the frequency response matrix, decomposition matrix dimensions and algorithm iteration number, corresponding to the high-spectral images, of a multispectral camera, wherein the low-resolution high-spectral image and the high-resolution high-spectral image are taken at the same height in the same target region at the same time; listing mathematical equations of the two processed images and a to-be-solved high-resolution high-spectral image, and building a Bayesian model; calculating the posteriori probability distribution of the decomposition matrix, and obtaining a matrix with the hidden spectrum features in the decomposition matrix, and solving the mean value of linear superposition vectors corresponding to the two images after preprocessing, thereby obtaining the to-be-solved high-resolution high-spectral image. The method greatly reduces the time consumption of calculation while improving the sharpening precision, and is easy to adjust.
Owner:TSINGHUA UNIV

Security hybrid encryption method based on narrowband Internet of Things

The invention provides a security hybrid encryption method based on narrowband Internet of Things, which comprises the following steps of carrying out lightweight identity authentication on each terminal and a server, and realizing the identity authentication of the terminal and the transmission of an initial key and an initial IV vector by using unidirectional asymmetric encryption in the lightweight identity authentication, and after the identity authentication of the terminal succeeds, performing encryption communication between the terminal and the server by adopting a dynamic symmetric encryption and decryption algorithm, so that the symmetric encryption and decryption algorithm uses different symmetric keys and different structural parameters in each time of encryption communicationof each terminal. Lightweight identity recognition is adopted; on the basis of ensuring the security, the authentication time and the calculation consumption are greatly reduced, a dynamic symmetric encryption and decryption algorithm is adopted, a symmetric key and a set of structural parameters are ensured to be set at a time, the structural parameters of the algorithm are changed in a nonlinearmode under the condition that the large structure is not changed, and the capacity of the algorithm for resisting side channel attacks such as energy analysis is improved.
Owner:SHANGHAI INST OF MICROSYSTEM & INFORMATION TECH CHINESE ACAD OF SCI

Gait recognition method based on two-dimension wavelet packet decomposition and complete PCA (Principal Component Analysis)

The invention discloses a gait recognition method, particularly the gait recognition method based on two-dimension wavelet packet decomposition and complete PCA (Principal Component Analysis), which belongs to the technical field of pattern recognition. The method comprises the following steps of: pretreatment (morphologic treatment, target extraction and image normalization), feature extraction (gait cycle, gait energy image and fusion of WPD plus (2D) 2 PCA selection feature) and classification of the test samples to a corresponding class according to the nearest neighbor classification principle. the method integrates periodic frames in one mean chart by utilizing the gait energy diagram so as to eliminate the influence of difference of periodic frame numbers on the feature extraction, thereby reducing the computational complexity; and besides, the method extracts and selects gait features by initially adopting the method of fusing WPD with (2D) 2 PCA so as to solve the problems of loosing of high-frequency components or excessive dimensionality due to the simple adoption of all data of the existing gait recognition method based on wavelet transformation, and has higher recognition rate as well as higher robustness of vision angle change.
Owner:BEIJING UNIV OF TECH

Graph neural network training method and device

The embodiment of the invention provides a graph neural network training method, and the method comprises the steps: obtaining a relation network graph, wherein each object node corresponds to a sampling probability set, and the sampling probability set comprises the sampling probability of each first-order neighbor node; performing multiple rounds of iterative updating on the graph neural networkon the basis of the relation network graph, wherein any round includes performing M-order neighbor node sampling by taking the first label node selected in the current round as a center, wherein anyi-order neighbor node sampling includes, for any first node in the sampled i-1th-order neighbor nodes, based on the current sampling probability set, sampling a plurality of neighbor nodes from a first-order neighbor node set, and classifying the neighbor nodes into an ith-order neighbor node; performing the current round of updating on the graph neural network based on the sampled neighbor nodeswithin the M order and the first service label carried by the first label node; and determining a plurality of training feedbacks corresponding to the plurality of neighbor nodes by using the updatedgraph neural network, and then updating the current sampling probability set of the first node.
Owner:ALIPAY (HANGZHOU) INFORMATION TECH CO LTD

Sum and product computing method for protecting data privacy security of arbitrary user group

The invention relates to a sum and product computing method for protecting data privacy security of an arbitrary user group. The computing method comprises the following steps of system initialization, user private key generation, secret key generation, sum encryption, product encryption, sum decryption, and product decryption. To be specific, according to the system initialization, a security parameter is specified; and a corresponding integer group and a system public key are generated by the security parameter. According to the user private key generation, participants in the user group compute private keys independently and respectively, so that the product of the private keys is equal to one after modulus operation. According to the secret key generation, a sum secret key and a product secret key for encryption data are generated for the participants based on combination of the system public key and the user private keys. According to the sum encryption, during the sum computing, the participants utilize the obtained secret keys to carry out encryption on the sum computing and send obtained sum ciphertexts to other participants in the user group. According to the product encryption, during product computing, the participants utilize the obtained secret keys to carry out encryption on the product computing and send obtained product ciphertexts to other participants in the user group. According to the sum decryption, the participants in the user group combine the received ciphertexts to obtain a final sum. And according to the product decryption, the participants in the user group combine received ciphertexts to obtain a final product.
Owner:TSINGHUA UNIV
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