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187 results about "Small data sets" patented technology

Human behavior recognition method based on attention mechanism and 3D convolutional neural network

The invention discloses a human behavior recognition method based on an attention mechanism and a 3D convolutional neural network. According to the human behavior recognition method, a 3D convolutional neural network is constructed; and the input layer of the 3D convolutional neural network includes two channels: an original grayscale image and an attention matrix. A 3D CNN model for recognizing ahuman behavior in a video is constructed; an attention mechanism is introduced; a distance between two frames is calculated to form an attention matrix; the attention matrix and an original human behavior video sequence form double channels inputted into the constructed 3D CNN and convolution operation is carried out to carry out vital feature extraction on a visual focus area. Meanwhile, the 3DCNN structure is optimized; a Dropout layer is randomly added to the network to freeze some connection weights of the network; the ReLU activation function is employed, so that the network sparsity isimproved; problems that computing load leap and gradient disappearing due to the dimension increasing and the layer number increasing are solved; overfitting under a small data set is prevented; and the network recognition accuracy is improved and the time losses are reduced.
Owner:NORTH CHINA ELECTRIC POWER UNIV (BAODING) +1

Image classification method and device

The invention discloses an image classification method and an image classification device. The image classification method comprises the following steps: based on a big image data set, training an AlexNet model structure; migrating the five trained convolution layers to a small database to form a lower-level feature extraction layer, and constructing together with a residual network layer, a multiscale pooling layer, a feature layer and a softmax classifier to obtain a migration model structure, wherein the residual network layer includes two convolution layers; inputting small image data set into the migration model structure, upgrading parameters by adopting a batch gradient descending method, and training an image classification hybrid model; and classifying according to the image classification hybrid model to obtain a classification result. By migrating the pretrained convolution layers on the big data set to the small data set, increasing the multiscale pooling layer, and serially connecting the feature quantity output by the residual network layer and the multiscale pooling layer and inputting to the classifier, the feature quantity is increased and the overfitting problem can be relieved; and through the hybrid model trained based on a convolution nerve network and a migration learning, the image classification accuracy can be effectively improved.
Owner:GUANGDONG UNIV OF TECH

High-resolution remote sensing image scene classification method for small data set

The embodiment of the invention discloses a high-resolution remote sensing image scene classification method for a small data set; and a three-scale three-channel end-to-end multi-scale convolution joint neural network model is established. The method comprises three steps: step one, random sub region extraction is carried out on a remote sensing image with an image size of N*N according three dimensions, wherein the dimensions of the sub regions of the image after extraction are N / 2*N / 2, N / 4*N / 4, and N / 8*N / 8 and inputs of a multi-channel convolution feature extractor; step two, a feature joint network based on multi-scale and multi-channel fusion is established and two-two fusion enhancement is carried out on features with different dimensions to realize a high-layer-feature joint enhancement expression; and step three, on the basis of a joint loss function, high-layer features of joint enhancement of the scene are classified. Therefore, high-precision classification of a small sample training set is realized; and the high-layer feature expression capability in the small sample data set and the computing speed are improved.
Owner:CHINA UNIV OF GEOSCIENCES (WUHAN)

Single classifier anomaly detection method based on multilayer random neural network

The invention discloses a single classifier anomaly detection method based on a multilayer random neural network. The the method comprises: only inputting a training data set of a normal class; through multilayer ELM-AE autoencoder and decoding processing, input sample data obtaining a reconstructed characteristic value; inputting the reconstructed characteristic value into the last layer of ELM to obtain actual output; sorting the obtained distance error vectors of the actual output and output tags from large to small, and determining a threshold for separating a normal class from an abnormalclass according to a set threshold parameter; and finally, inputting the test data into the multi-layer random neural network single classification abnormity detection model, and testing the recognition effect of the model. According to the method, main information is extracted more quickly and efficiently, dimensionality reduction is carried out, and then recognition and classification are carried out. And the speed is higher, the accuracy is higher, and the generalization performance is better. The method is not only suitable for small data sets, but also suitable for high-dimensional largedata sets, and has universality. And the method has important significance for practical application in future.
Owner:HANGZHOU DIANZI UNIV

An image advanced semantic recognition method based on multi-feature fusion of a deep network

The invention provides an image high-level semantic recognition method based on multi-feature fusion of a deep network. A global color histogram extracts the color features of an image, the LBP algorithm extracts the texture feature of the image, the deep object network extracts the object features of the image and the deep emotion network extracts the deep emotion features of the image to be fused to identify the composite emotion of the image and the main object contained, Finally, for the input image, the network model can generate descriptive phrases with high-level semantic information, including emotional semantics and object semantics. The invention starts from the aspect of depth learning for small data sets. In this invention, a new method of data expansion is proposed, which combines pre-extracted low-level features such as color and texture statistics, and proposes a multi-feature fusion method to identify the emotion of images and the high-level semantic information of objects. This method improves the accuracy of the experimental results and makes the selection of experimental images more scientific.
Owner:TAIYUAN UNIV OF TECH

Hadoop framework-based short-term load prediction method for distributed BP neural network

The invention discloses a Hadoop framework-based short-term load prediction method for a distributed BP (Back Propagation) neural network. The method specifically comprises the steps of obtaining an initial load data set; dividing the load data set into small data sets and storing the small data sets in data nodes of a distributed file system; initializing BP neural network parameters and uploading a parameter set into the distributed file system; training the BP neural network according to a current load sample, and obtaining correction values of a weight and a threshold of the BP neural network in the current data set; performing statistics on sum of weight and threshold parameters of all layers and between the layers of the network according to a key value of a key value pair; judging whether the convergence precision or the maximum iterative frequency is reached or not in a current iterative task, and if yes, establishing a distributed BP neural network model, or otherwise, performing correction of the weight and threshold parameters of the network; and inputting prediction day data and obtaining load power data of a prediction day. According to the method, the load prediction speed is increased and the requirements of load prediction precision are met.
Owner:SICHUAN UNIV

Pick release performance using a multi-threaded architecture to distribute the workload across multiple processors

In various embodiments, methods and systems can provide highly efficient order processing suitable for high volume order processing businesses. A batch of orders can be preprocessed to create smaller data sets that group orders for the same item together. The smaller data sets can then be analyzed and processed in parallel without causing database locks. Thus, the time required to complete the order fulfillment cycle may be reduced, which may in turn, increase one or more of operation efficiency, customer satisfaction, sales volume and profits.
Owner:ORACLE INT CORP

Parallelization method of convolutional neural networks in fuzzy region under big-data environment

The invention discloses a parallelization method of convolutional neural networks in a fuzzy region under a big-data environment. The parallelization method comprises the following steps: firstly, constructing the convolutional neural networks in the fuzzy region, putting a given target assumption region and object identification into the same network, carrying out convolutional calculation, and updating the weight of the whole network in a training process; and secondly, dividing an input log data set into a plurality of small data sets, introducing multiple workflows to pass through the convolutional neural networks in the fuzzy region in parallel for convolution and pooling, and independently training each small data set by virtue of gradient descent. By virtue of the parallelization method, a network structure and parameters are optimized, and relatively good analysis performance and precision are realized; furthermore, the number of FR-CNN obfuscation layers is adjusted aiming at different log data sets, so that the extracted features can well reflect the characters of oil-gas reservoirs, and the fuzzification problem of the log data can be solved; and the parallel training and execution of FR-CNN are carried out by virtue of multiple GPUs, so that the efficiency of the FR-CNN is improved.
Owner:CHINA UNIV OF PETROLEUM (EAST CHINA)

Face recognition method based on convolutional neural network

The invention relates to a face recognition method based on the convolutional neural network. The problems that an existing face recognition method for a small data set is low in accuracy and poor inself-adaptation and needs manual parameter regulation are solved. The method comprises the steps of 1, dividing face images needing to be recognized into training samples, testing samples and verification samples, and reading data to be trained; 2, performing normalization processing; 3, constructing a network structure of the convolutional neural network; 4, placing the training samples into thenetwork structure constructed in the step 3 for training, wherein the training process includes forward broadcasting and reverse broadcasting; 5, saving model parameters trained in the step 4; 6, adopting the trained model parameters for detecting the testing samples obtained after normalization processing in the step 2, and obtaining a model detection result. The method is used for the field of pattern recognition.
Owner:YANSHAN UNIV

Large-scale malicious domain detection system and method based on self-feedback learning

The invention discloses a large-scale malicious domain detection system and method based on self-feedback learning and relates to the technical field of computer network security. For the deficiency of an existing detection technology on mass data processing and detection model updating, a malicious domain real-time detection system applicable to large-scale data is designed and realized. A methodof extracting a small data set for verification and updating is provided innovatively. The online learning efficiency is improved. Core algorithms comprise an algorithm of detecting malicious domainsbased on a support vector machine (SVM) in mass real-time domain detection, an online learning algorithm fSVM based on the self-feedback learning and an automatic calibration algorithm. Through theoretical demonstration and experimental verification, according to the algorithms provided by the invention, when the newly-presented malicious domains are copied with, the response can be carried out timely, and the excellent operation efficiency is achieved. According to the system and the method, the further analysis of the detected malicious domains is also realized. The system and the method play an enlightening role in perceiving malicious domain related threat intelligence.
Owner:SHANGHAI JIAO TONG UNIV

Comparison detector and building method thereof as well as cervical cancer cell detection method

The invention provides a comparison detector and a building method as well as a cervical cancer cell detection method. The method applying the comparison detector to detect cervical cancer cell comprises the following steps that firstly, a class reference sample is selected by applying a t-SNE (t-distributedstochastic neighbor embedding) visualization method, and then the features of the class reference sample and a target detection image are extracted by applying a feature extraction network which is composed of a basic convolution network and a pyramid convolution network in the comparison detector; the extracted feature of the reference sample is processed by applying a reference sample processing module so as to obtain prototype expression of each class; a recommended region is obtained by applying a region recommending module, and features are obtained from a corresponding feature pyramid; and finally, the class of the recommended region is obtained by comparing the pyramid feature of the recommended region and the prototype expression of class, and fine tuning is conducted on the rectangular frame by utilizing the features of a candidate region. The method builds a target detection network which detects targets on small data sets, and can relieve the overfitting problem of the target detection network on the small data sets.
Owner:湖南品信生物工程有限公司

Scheduling and information sharing and distribution system and method

A system and method for coordinating online and offline schedules. The system allows a user to streamline calendar information from various online and offline sources into one distribution channel. The system and method provides an online interface with numerous outlets to other calendar systems, without the need for a license for each additional calendar system. In general, the system is capable of maintaining varying capacity and is scalable. As such, the system is able to manage huge variation and user populations in a small data set. Information can be standardized to be entered into the system only once and subsequently be distributed to various types of programs and platforms.
Owner:FAY WILLIAM F +1

Plant disease and insect pest identification method and system based on deep learning

The invention discloses a plant disease and insect pest identification method and system based on deep learning. The plant disease and insect pest identification method comprises the steps: (1) receiving an original training image which comprises various types of plant disease and insect pest image data; (2) preprocessing based on an image preprocessing mode selected by a user for the training image; (3) inputting the preprocessed image into a deep learning model according to disease and pest categories, wherein the deep learning model comprises a convolution layer, a residual attention mechanism model and a full connection layer which are connected in sequence; and visualizing the output of the residual attention mechanism model to serve as a new training image, and returning to the step(2); (4) training a deep learning model based on all the training images; and (5) identifying diseases and insect pests. According to the invention, targeted enhancement and expansion processing are carried out on the small data set by using a method of combining data enhancement and an attention mechanism, so that the plant disease and insect pest identification accuracy can be effectively improved.
Owner:SHANDONG AGRICULTURAL UNIVERSITY
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