40results about How to "Realize automatic learning" patented technology

The invention relates to a device for detecting network access abnormality based on data stream behavior analysis, comprising a flow information collection module, an abnormal behavior detection module and an abnormal flow processing module, wherein the flow information collection module is respectively connected with the abnormal behavior detection module and the abnormal flow processing module;and the abnormal behavior detection module is connected with the abnormal flow processing module. The invention also relates to a method for using the device. In the method, obvious abnormal flow data is filtered out firstly, then a network behavior model is used to detect the filtered flow data, and the network behavior model is automatically updated; and finally, the flow is blocked according to detection results. The device and method provided by the invention is utilized to establish a normal network behavior model. The model is compared with real-time data so as to detect whether real-time flow is abnormal; and the network behavior model is dynamically modified, abnormal flow sources are analyzed, and the abnormal flow is blocked, thus identifying the abnormal flow quickly and effectively and improving the accuracy of the detection.

The embodiment of the invention discloses a building extraction method based on a gating depth residual error optimization network. The method comprises the steps of obtaining an image feature combination of a high-resolution aerial image and airborne LiDAR point cloud data; the diversity of image samples is enhanced through the modes of random cutting, rotation, overturning and light and shade adjustment; using the improved deep residual convolutional neural network to automatically learn multi-level features of the image to obtain a rough building extraction result; a gating feature markingunit is adopted to screen and fuse effective features, and a high-quality building extraction result is obtained through successive up-sampling. By implementing the embodiment of the invention, a feature information gating transmission mechanism is combined with the deep residual convolutional neural network, and the method is used for building extraction of high-resolution aerial images and airborne LiDAR point cloud data.

The embodiment of the invention provides a human face recognition method and terminal equipment. Image statistics features are used for comparing target human face image frame sequences with picture sets in a human face sample bank, if the fact that a sample human face matched with a target human face is unique is know by judging and the proportion of picture frames which are successfully matched in the picture frame sequences is larger than a preset first threshold value and smaller than a preset second threshold value, picture frames which are not matched successfully are added into the human face sample bank and a picture set corresponding to a sample human face, wherein the second threshold value is larger than the first threshold value. An automatic learning and amending process of the human face sample bank is achieved, the problem that a user carries out complex amending operation is avoided, along with accumulation of using time, human face recognition accuracy can be higher, and the practicability of a human face recognition system is improved.

The invention relates to the technical field of image segmentation, and provides a segmentation method of an iris region in an iris image based on a Mask R-CNN neural network which comprises the stepsof S100, S100, establishing an improved Mask R-CNN neural network for iris segmentation S200, before training the neural network, marking a sample iris image set for training; S300, respectively inputting the labeled sample iris images into a neural network, and training the neural network until convergence; S400, inputting the iris image that needs to be segmented by the iris region into the improved Mask R-CNN neural network that completes the training, and acquiring the double circle boundary information and the binary mask map of the iris region; and S500, completing iris segmentation according to the double-circle boundary information of the iris area obtained in the step S400 and the binary mask map. The invention realizes automatic learning to obtain a normalized iris image and aniris region binary mask by using the improved Mask R-CNN neural network in iris segmentation and localization. Accurate iris area double-circle boundary information can also be obtained, and subsequent iris recognition operation is facilitated.

The invention relates to the field of cloud computing and computer networks, and specifically provides a method for adding public cloud network physical host into VPC. The method comprises: the physical host being connected to a ToR switch through a vlan, the ToR of the physical host supporting the vxlan and multicast, and expanding a plugin and an agent of the neutron; adding a plugin of l2 gateway into a new server, and expanding the function of the generic network switch plugin, so as to obtain the plugin of l2 gateway; and configuring vni mapping of vlan and vxlan of a ToR switch where a physical host is located, newly adding agent extension of l2 gateway in an openvswitch agent, and managing a virtual network bridge and an ovs flow table by a user. Compared with the prior art, the physical host and the VPC internal virtual machine can achieve two-layer and three-layer intercommunication through the VPC internal network address, unified management of the virtual machine and the physical host is achieved, and good popularization value is achieved.

The invention discloses a credit customer qualification classification method based on WOE conversion through machine learning. A system comprises a data preparation and preprocessing module, a modeltraining and evaluating module, a model deployment module, an inlet data processing module and a client qualification division module. The data preparation and preprocessing module is used for calculating original data I from the application data, the credit investigation data and the call record, calculating original data II through the customer category and the repayment data, and carrying out data preprocessing on the original data I and the original data II. The invention relates to the technical field of qualification classification. The credit customer qualification classification methodbased on WOE conversion through machine learning provides the system for realizing customer classification with different qualifications based on the machine learning method, the workload of manual auditing can be reduced, the approval efficiency is improved, learning is performed in time according to newly added customer information, self-adaption to customer qualification change is realized, the manual auditing efficiency can be improved to a greater extent, and the labor cost is reduced.

The invention provides a topology learning method, device and system of a one-way serial bus network. The topology learning method of the one-way serial bus network comprises the steps that a master node device sends a topology learning instruction message, wherein the topology learning instruction message is used for instructing a slave node device to read a node number of the slave node device in the topology learning instruction message, update the node number after one node is added, add the updated node number, the address thereof or the ID thereof to the topology learning instruction message, forward the topology learning instruction message after finishing the addition to a next-hop slave node device of the slave node device when the slave node device serves as an intermediate slave node device, and return the topology learning instruction message after finishing the addition to the master node device when the slave node device is the last-hop slave node device; and the master node device receives the returned topology learning instruction message and determines a topology structure of a one-way ring network based on the node number, the slave node device address or ID in the returned topology learning instruction message. According to the embodiment, the network topology structure can be learnt automatically, and the change of the network topology structure can be accordingly perceived.

The invention discloses an image feature fusion method and device, and a medium. The image feature fusion method comprises the following steps: inputting at least two images with an association relationship into a feature extraction network; respectively extracting feature information corresponding to each of the at least two images through the feature extraction network; providing the extracted feature information as point feature information of each node in the graph convolutional neural network; determining the fused feature information of each node through a convolutional neural network and based on the point feature information of each node and the edge feature information of the edge connected with each node; based on the fused feature information of each node, updating the feature information corresponding to each of the at least two images to obtain fusion feature information corresponding to each image, the edge feature information being a weight value indicating the degree ofdependence between two connected nodes, and the degree of dependence comprising at least three different levels.

The invention relates to the technical field of credit default prediction, in particular to a machine learning interpretability-oriented credit default prediction method, which comprises the following steps of S1, acquiring data; s2, preprocessing the data; s3, dividing and training data; s4, verifying the model. The invention further discloses a system of the credit default prediction method oriented to machine learning interpretability, the system comprises a data acquisition module, the data acquisition module is connected with a cleaning and screening module through a signal line, the cleaning and screening module cleans the input data, and if a certain variable of the data is missing, a few non-core data is deleted; if the deletion amount is large, the data is filled by an overall distribution sampling method and a method of performing maximum likelihood estimation according to other information, and the cleaning and screening module is connected with a data division module through a signal, so that default prediction can be performed efficiently and accurately.

The invention relates to a split type robot for automatically collecting a respiratory tract specimen by teleoperation. The robot comprises a robot body, the robot body is provided with a fixed arm and an operation arm. The fixed arm comprises first X-direction guide rails distributed in pairs. A forehead pad height adjusting mechanism and a denture ring height adjusting mechanism are installed onthe first X-direction guide rails respectively. A forehead pad assembly is arranged on the forehead pad height adjusting mechanism. A pillow plate is arranged between the first X-direction guide rails. The operation arm comprises second X-direction guide rails distributed in pairs. A sheath and lifting mechanism and a hose fixing and propelling mechanism or a throat swab and connecting pipe are arranged on the second X-direction guide rails. An endoscope lens is installed on the sheath and lifting mechanism, the throat swab and connecting pipe is installed on the hose fixing and propelling mechanism, and a controller is electrically connected to the robot body. Therefore, under monitoring of the endoscope lenses and cooperation of the controller, local automatic operation or remote control can be achieved, and the exposure risk of medical staff during work is reduced or even avoided.

The invention belongs to the technical field of disc shear tool detection, and discloses a disc shear tool changing opportunity prediction system and method based on shear blade wear detection, and the method comprises the steps: a detection system carries out the collection of a disc shear blade part image, carries out the feature extraction, and calculates the wear loss of a current disc shear tool; and the expert system calculates a tool abrasion loss-steel passing amount curve of the disc shear under different process parameters based on the collected data, and predicts the tool changing time of the tool under different process parameters. The detection system comprises a high-precision and high-speed industrial camera for acquiring an image of the cutting edge part of the circle shear, an image storage platform for storing the acquired image of the cutting edge part of the circle shear, and an image processing platform, according to the method, the workload of technicians is reduced, the accuracy of the disc shear tool changing time is improved, the time point is set to dynamically monitor the disc shear tool, and an accurate basis is provided for tool changing.

The invention discloses a method for realizing credit customer qualification classification based on WOE conversion. The method comprises the following steps of data preparation and preprocessing, model training, model evaluation, model deployment, incoming data processing and a customer qualification division module. The invention relates to the technical field of credit. According to the methodfor realizing credit customer qualification classification based on WOE conversion, the noise influence is reduced, meanwhile, the dimensionality of non-numerical data conversion is smaller than thatof ONE _ HOT conversion, and the purposes of automatic model learning, more sensitive customer data change and higher prediction accuracy are realized.

The invention relates to a pepper leaf disease detection method based on improved AlexNet, and the method comprises the steps: building a disease data set, carrying out the data enhancement of the disease data set, carrying out the disease type classification of the image data of the disease data set, marking a corresponding label, building a model data set, and carrying out the detection of a pepper leaf disease. Dividing image data of the model data set into a training set, a verification set and a test set in proportion; secondly, constructing a convolutional neural network model, performing feature extraction on an AlexNet model in the convolutional neural network model, setting a multi-scale convolution kernel for a first convolutional layer, removing a full connection layer, replacing the full connection layer with a global average pooling layer, adding a BN layer into each convolutional layer, then setting hyper-parameters, and obtaining a multi-scale convolution kernel; and training the AlexNet model by using the training set. According to the improved AlexNet-based pepper leaf disease detection method provided by the invention, the model can be reduced, the identification precision can be improved, and the detection speed can be improved.