36 results about "Bidirectional map" patented technology

A central determination gaming system with a keno game. Upon a player initiating a game play at a gaming terminal, the gaming terminal enables the player to select a plurality of symbols. The gaming terminal accesses a standard set of predefined symbols for the specific game played. The gaming terminal bidirectionally maps the player's selected symbols with the standard set of predefined symbols. The gaming terminal receives a predetermined game outcome seed from a central controller. The gaming terminal utilizes the selected game outcome seed to generate a plurality of game symbols. The gaming terminal determines if each of the generated game symbols needs to be modified. If so, the gaming terminal modifies these generated game symbols based on the bidirectionally mapping, presents the modified game symbols to the player and provides the player a game outcome that is determined based on the selected game outcome seed.

A data processing system for emulating a desktop application desktop software applications in a mobile communication network that comprises (a) an agent for identifying and activating UI objects of a desktop application, and changes in the UI objects (b) a Designer-Tool that is associated with the agent, to generate offline (b.1) a plurality of data sets including representations for objects and states of the desktop application to be converted to corresponding mobile objects and states; (b.2) a plurality of representations of required changes in objects in response to user actions; (b.3) a plurality of data sets for bidirectional mapping between the objects and states of the desktop application and the corresponding mobile objects and states; (c) a user client to control the GUI and represent objects and states of the desktop application and to receive user actions from the GUI; change the represented objects responsive to user actions; and send the user actions to a gateway that is in communication with the user client and with the desktop application via the agent, and during runtime, to (d.1) map the user actions from the user client to corresponding actions in the desktop application to be used by the agent using the datasets; (d.2) receive updated outputs from the desktop application via the agent; (d.3) map the updated output and UI objects to a mobile format, using the datasets; (d.4) send the mapped updated output to the user client; (d.5) manage sessions of users with the desktop applications in the form of service points running on the server or desktop computer.

The invention discloses a point cloud feature space representation method for laser SLAM, and the method builds the bidirectional mapping of a point cloud space and a point cloud feature space based on a deep learning network, and achieves the closed-loop detection and repositioning in the laser SLAM and the map compression storage and transmission in the feature space. Global description featureextraction and compression reconstruction of the large-scale scene point cloud are realized through a neural network of an auto-encoder structure; an encoder network is designed to extract global description features to form a feature space of the point cloud, and similarity measurement of scenes is given according to the distance in the feature space and used for judging whether two or more scenestructures are similar or not, so that closed-loop detection and repositioning of laser SLAM are realized; the method includes reconstructing the point cloud through the designed decoder network, reconstructing the original point cloud from the global description features extracted from the encoder network, and realizing compressed storage and low-bandwidth transmission of the point cloud map; the constructed encoder network does not need to be trained in advance according to a point cloud map, and has strong generalization ability.

The invention discloses a dual learning-based cross-modal text video generation method and system, and the method comprises the following steps: constructing a text-to-video generation model; constructing a video-to-text mapping model; utilizing a dual learning mechanism to jointly train the generation model and the mapping model to obtain a training model; inputting preset characters into the training model to generate a corresponding initial video; and mapping the initial video into new characters by utilizing the mapping model, and feeding back the new characters to the generation model tojudge whether the new characters are matched with preset characters or not, and then repairing the initial video to obtain a final mapped video. The bidirectional mapping between the text informationand the video information is considered, so that the text-to-video generation is better realized, and meanwhile, the generated video is higher in quality and higher in matching degree with user requirements.

The invention relates to a search method and system based on bidirectional mapping. By adopting first-grade mapping and second-grade mapping, on one hand, the problem that a user is lack of legal knowledge and cannot position own actual retrieval demand requirements very well is solved; on the other hand, the problem that the evaluation of lawyers practice conditions is not comprehensive, caused by a fact that determination and expressions of causes of action for administration cases in local courts of justice are different, is solved; on the basis of mapping in the two aspects, the butt joint of a user retrieval demand and a retrieval object is further realized, so that the user retrieval demand is accurately positioned, the accuracy of a retrieval result and the data retrieval efficiency are improved.

The invention discloses a bird endangered species identification method based on structure-preserving zero sample learning, and the method comprises the following steps: a data input step: inputting adata set which comprises the visual features, semantic information and label information of a common bird image, and the semantic information of bird endangered species; and a training step: learningbidirectional mapping from a visual feature space to a semantic space, and meanwhile, and further constraining the mapping by using manifold consistency, wherein the optimization problem is summarized into a Sylvester equation solving problem, the solving process is simple and easy to implement, and the solving result is a mapping matrix P; and a prediction step of identifying the bird endangeredspecies image with the given semantic information by using the mapping matrix P obtained in the training step. According to the method, structural information between data is reserved, the problem ofdomain drift is solved, the image classification accuracy is improved, the method can be applied to complex bird image recognition, and endangered species without known label information can be recognized.

The invention discloses a layered database model based on material-structure-process-process correlation, which is used to solve the technical problem of poor practicability of the existing layered database model. The technical solution is to divide the working conditions along the machining trajectory according to the tool-workpiece contact state, part structure and position relationship, and correspond the part structural features with the processing technology and process parameters. In terms of data storage, it is divided into cutting basic data layer, process basic data layer and process data layer. The cutting basic data layer stores data such as tool coating, cutting edge geometry, and constitutive equations; the process basic data layer adds tool parameters, process methods, and tool-workpiece meshing relationships on the basis of the cutting basic data layer; the process data layer stores additional tools ‑Spindle subsystem information, workpiece‑fixture subsystem information, processing environment information and CNC system information. The invention realizes the two-way mapping between different levels of data, and has good practicability.

The invention relates to a method for establishing bi-directional mapping among sequences under the condition of difference limitation. According to the method for establishing bi-directional mapping among sequences under the condition of difference limitation, two groups of discrete data are arranged into two sequences from small to large respectively, the two sequences are stored in two comparison modules, are pointed in sequence and compared through data item vernier variable modules CurX and CurY, if two data which meet the condition of difference limitation and are respectively located in the two sequences exist, a bi-directional mapping relation is established and stored, finally data items with the bi-directional mapping relation in the two sequences are counted, and therefore the purposes of discovering the relevance and similarity of the two groups of data are achieved.

The invention discloses a method and system for generating video from cross-modal text based on dual learning, wherein the method includes the following steps: constructing a text-to-video generation model; constructing a video-to-text mapping model; using a dual learning mechanism to combine Train the generative model and the mapping model to obtain the training model; input the preset text into the training model to generate the corresponding initial video; use the mapping model to map the initial video to new text, and feed back the new text to the generative model to judge the new text. Whether the text matches the preset text, and then repair the initial video to obtain the final mapping video. This method considers the bidirectional mapping between text information and video information to better realize the generation of text to video, and at the same time, it also makes the generated video higher in quality and more closely matched with user needs.

The invention discloses a material-structure-technology-process correlation-based hierarchical database model, which is used for solving the technical problem of poor practicality of an existing hierarchical database model. According to the technical scheme, working condition division is performed according to a tool-workpiece contact state and a part structure and position relationship along a processing track; and part structure features are enabled to correspond to a processing technology and process parameters. In the aspect of data storage, the data storage is divided into a cutting basicdata layer, a technology basic data layer and a technology process data layer. The cutting basic data layer stores data such as a tool coating, cutting edge geometry, a constitutive equation and thelike; the technology basic data layer adds tool parameters, a technology method and a tool-workpiece meshing relationship based on the cutting basic data layer; and the technology process data layer stores and adds information such as tool-spindle subsystem information, workpiece-clamp subsystem information, processing environment information, numerical control system information and the like. Thebidirectional mapping among different hierarchical data is realized; and the practicality is high.

The invention provides a medical image on-chain representation and under-chain secure edge storage method based on a block chain technology, a trusted computing technology and a compressed sensing encryption and decryption image technology. Generating a first hash chain and a second hash chain according to the initial key by using an image encryption method based on hash chain compressed sensing; and obtaining a measurement matrix and an encryption matrix according to the first hash chain and the second hash chain, obtaining a measurement result matrix through the original image and the measurement matrix, and obtaining an encryption image matrix through the measurement result matrix and the encryption matrix. And hashing the measurement matrix again to obtain a hash value of the measurement matrix, and storing the medical image related information, the hash value of the measurement matrix and the node signature into the block chain. And storing the encrypted image matrix in a node local space. In the whole process, existence proof, namely on-chain representation of the medical image, of the medical image is achieved, the image is safely stored in a node local space, namely under-chain safe edge storage is achieved, and bidirectional mapping of the medical image and the under-chain safe edge storage is achieved through uniquely-identified medical data information and the hash value of an image measurement matrix.

The invention discloses a taxpayer industry classification method based on a multistage generative model, which comprises the following steps: firstly, extracting to-be-mined text and non-text information in taxpayer industry information to carry out text embedding and coding, and carrying out feature processing on the coded information; secondly, converting the taxpayer industry category label with noise into a multi-complementary label; thirdly, constructing a multi-level generation model of label and feature levels under the bidirectional mapping framework; then, training the device based on the encoded features and the generated multiple complementary tags; and finally, taking the prediction of the label level on the real label as the final taxpayer industry category of the test data. According to the method, the noise rate of the tag is reduced by converting the noisy tag into the multi-complementary tag, the feature and tag level bidirectional mapping is introduced to cope with feature-dependent noise existing in the taxpayer industry category tag, and the taxpayer industry classification accuracy can be effectively improved.

The invention discloses a super-lens chromatic aberration recovery method based on deep learning. The method comprises the following steps: 1) constructing a super-lens chromatic aberration recovery neural network and a chromatic aberration recovery loss function; 2) taking the super-lens chromatic aberration image x and a standard image y corresponding to the super-lens chromatic aberration image x as a training data pair, training the super-lens chromatic aberration recovery neural network to enable a chromatic aberration recovery loss function value to be less than 8, realizing bidirectional mapping from the super-lens chromatic aberration image to the standard image corresponding to the super-lens chromatic aberration image, and obtaining a trained super-lens chromatic aberration recovery model; and 3) inputting the to-be-recovered super-lens chromatic aberration image into the trained super-lens chromatic aberration recovery model by using the step 2) to realize super-lens chromatic aberration recovery.

The embodiment of the present application provides a configuration method, device, terminal and storage medium of a visual development environment, the method includes: binding a physical data model, reading the physical data model, establishing an O / R relationship channel; loading the model data set, Configure the dependency relationship and event relationship between data models to generate a two-way mapping relationship with the physical data model; carry out secondary structural transformation of the data model set to complete the field-level mapping relationship; according to the physical data model and data model set O / R two-way mapping relationship to generate a unified code file; call the data interface, access the database, and complete the functional operation of the business scenario; the method provided by this application unifies the technical framework, and at the same time, the technical framework has high automation, high development efficiency, and low professional requirements. , Few loopholes, standardized code, safe, easy to use, and comprehensive functions, saving time and labor costs.