190 results about "Single label" patented technology

The invention discloses a positioning and navigation control method of an indoor mobile robot based on UWB. The method comprises the steps UWB signal flight time is obtained independently through UWB communication chips arranged on tags and base stations, and then the flight time is multiplied by signal propagation speed so that the distance between one single tag and one single base station can be obtained; the tags are positioned by adopting a TOF positioning method, and distance measurement is performed through the UWB signal flight time according to the set mechanism; when the effective distances between the tags and four base stations (including one communication base station) are obtained, the three distances with the high credibility are selected, the coordinates of each base state are used as a circle center, the corresponding distance is used as a radius, and the coordinate position of the corresponding tag is calculated by means of the circle intersection point. By means of the UWB technology, wireless positioning control over the indoor robot is performed, and the higher positioning precision and stability can be obtained on the basis of the advanced distance measurement method.

An embodiment of the method of the invention is a method of automating information flow in a laboratory performing tissue staining comprising positioning a networked label printer adjacent to a cutting station, the printer configured to access patient data directly or indirectly from the hospital LIS, the printer being configured with a data element scanner in electronic communication with said printer; inputting data from a tissue cassette-associated data element at said printer, whereby inputting data comprises reading the data from the cassette-associated data element and uploading the cassette data to the LIS; identifying the corresponding test protocol identifier and then downloading the test protocol data to the printer; printing information on labels corresponding to each test specified in the LIS for the patient; attaching a single label to each slide; and cutting a tissue section for each labeled slide and mounting the section on the slide.

Multi-mode (e.g., EAS and RFID) tags and methods for making and using the same are disclosed. The tag generally includes an antenna, an electronic article surveillance (EAS) function block coupled to the antenna, and one or more identification function blocks coupled to the antenna in parallel with the EAS function block. The method of reading the tag generally includes the steps of applying an electric field to the tag, detecting the tag when the electric field has a relatively low power, and detecting an identification signal from the tag when the electric field has a relatively high power. The present invention advantageously enables a single tag to be used for both inventory and anti-theft purposes, thereby improving inventory management and control at reduced system and/or “per-article” costs.

The systems and methods described herein relate generally to the integration of a shipping label with a customs form to provide a single label having postage, addressing, customs, and other information for international shipping, and in particular, to managing landed cost and harmonization for international packages. In one embodiment, managing the landed cost and harmonization may comprise generating a unique self-validating postage indicium or mark in response to a request that includes a description of contents for a package onto which an international shipping label is to be placed and a destination country where the package is to be delivered. As such, the unique self-validating postage indicium or mark may indicate that landed costs for delivering the package to the destination country have been pre-paid, wherein the unique self-validating postage indicium or mark and a customs declaration form may be printed on the international shipping label.

The systems and methods described herein relate generally to the integration of a shipping label with a customs form to provide a single label having postage, addressing, customs and other information for international shipping. In one embodiment, a method for generating an international shipping a package comprises the steps of providing shipping information associated with the package, the information comprising address information of the origin, address information of the destination, postage information including a postage amount due to a post service and a customs form number unique within the post service; processing a shipping label having the shipping information displayed thereon; and affixing the shipping label to the package.

The invention relates to a mechanism for enhancing database performance in a Domain Name System (DNS). In order to optimize the performance of current de-facto DNS name servers for queries that request data related to a E.164 number, a domain name received in a first format is converted into a second format before it is supplied to database operations. In the second format at least two successive labels of the domain name are combined to form a single label. The conversion may be made in connection with a zone transfer and in connection with a subsequent DNS query.

The invention relates to an image combined with deep learning and hashing methods. And a text cross-modal retrieval model. The invention provides a deep cross-modal Hash algorithm based on multilayersemantics in order to solve the limitation that a traditional cross-modal Hash method based on deep learning directly converts multi-label data into a single label problem when processing the multi-label data problem. And the similarity between the data is defined through a co-occurrence relation between the multi-label data, and the similarity is used as supervision information of network training. And designing a loss function which comprehensively considers multi-layer semantic similarity and binary similarity, and training the network, so that the feature extraction and Hash code learningprocesses are unified in one framework, and end-to-end learning is realized. According to the algorithm, semantic correlation information between the data is fully utilized, and the retrieval accuracyis improved.

Techniques are provided for ensuring lexical fidelity when an XML document is stored in a binary format. Operations, on the XML data, that would cause the loss of lexical fidelity between the original XML document and the binary-encoded version of the XML document are not performed. Such operations include the removal of unnecessary whitespace characters, certain data type conversions, CRLF normalization, the “collapsing” of two-tag empty elements into a single tag empty element, and the replacing of entity references or numeric character references with another value. An XML schema, to which the XML document conforms, may indicate that the XML document is to be stored in a lexical fidelity mode. Additionally, or alternatively, the database statement that (when executed) causes the XML document to be stored in a binary format may so indicate.

The invention relates to a device for assisting judicial case judgment based on machine learning, which utilizes a large amount of document data and trains a model to learn the relationship between case fact description and the fine range and relevant legal provisions, and realizes the prediction of the fine range and the law label of any given case fact description text. The invention relates toa device for assisting judicial case judgment based on machine learning. Including: defining the proper nouns in the description of the facts of a given case and dealing with them; Extracting multiplesemantic features from the text to achieve a deeper level of semantic representation; Machine learning method based on multi-label classification is used to classify the law items and obtain the lawlabels related to the description text of the case facts. Single-label classification training model based on machine learning predicts the range of possible fines in related cases. The invention applies machine learning to the judicial field for the first time, realizes deeper semantic representation by multiple feature extraction modes, improves the accuracy and generalization ability of the training model well, has higher reference significance for the final judgment of a case, and is conducive to the realization of the same case and the same judgment.

A system and method are provided for associating a plurality of objects using a single RFID tag. The RFID tag includes a first page and a second page. An interrogator communicates with a server to interrogate the first page and the second page, wherein the first page and the second page include data that identifies objects to be tracked by the radio frequency identification tag and wherein the identified objects are associated based on a relationship between the first page and the second page.

System for managing a collection of objects, for example in a store, employing identification tags (RFID) assigned to object (O) locations and individual labels (ID-O) associated with the objects. The objects are identified using a portable label scanner (RFIDL) operating in either a local mode (M1) or in a single mode (M2), with said scanner being connected to a control unit (CPU) that controls the operation of the scanner. The local mode (M1) consists of reading the label (ID-P_j) of the display location (P_j) of a batch of objects (Oi) and of scanning the label of the objects located in this location for comparison and verification. The single mode (M2) is a close-up scan of the label of an object, taken individually.

Embodiments of the invention provide an image label determination method and device and a terminal. The method comprises the following steps of: inputting an image into a convolutional neural networkand determining a feature map of the image; inputting the feature map into a classifier, wherein the classifier comprises a plurality of classification tasks, and each classification task is a multi-label classification task or a single-label classification task; respectively predicting labels corresponding to the feature map through each classification task in the classifier, wherein each label corresponds to a probability value; and screening a label of the image from the predicted labels according to the probability values. The image label determination method provided by the invention is capable of adaptively adjusting image classification manners according to image types, is strong in flexibility and is capable of enhancing the correctness of label prediction results.

Systems, methods and articles of manufacture are disclosed for resolving hostnames on a private network with a distributed DNS server configuration. One embodiment of the invention provides for a client resolver configured to communicate with a distributed network of servers. The client resolver receives a hostname query and determines if the hostname query is of the type single lablel hostname or fully qualified hostname. If the hostname query received is of the type fully qualified hostname, the client resolver routes the hostname query to a server with a longest matching domain suffix. If the hostname query received is of the type single label hostname the client resolver generates one or more new fully qualified hostnames and routes the hostname query to each server on the private network.

The invention provides a parallel random label subset multi-label text classification method for Spark-based big data platform. First of all, large scale text data sets and configuration information files are read, the distributed data set RDD is created, the training data set and the prediction data set are cached in memory to complete the initialization operation. Secondly, the label subset of the required numbers is randomly generated in parallel, a new training set is generated for each label subset by the original training set, once again, multiple tags of the new training set through the tag power set are converted into single labels, the data sets are converted into single label multiple data sets, and a base classifier is trained in parallel for these data sets. Then the single label multiple prediction results obtained by prediction are converted into multi-label results. Finally, all the predicted results are collected and voted, to obtain the final multi-label prediction results of the test set. The multi-label text classification method improves the classification accuracy, dramatically reduces the learning time of handling large scale multi-label data.

The invention provides a label retransmission method and a device thereof. The method comprises the following steps: a first layer of label is received as a message of a first label; a following data unit adjacent to the first label is taken as a label or an IP message; and the first label is popped up and message following the first label popped up is transmitted to a loopback port. The method is used for meeting the requirement of processing message of a plurality of labels and a single label at the same time when an exchanger cannot support PHP.

The invention provides a no-body-paper label as well as a process method and a label roll thereof. A baseband layer is subjected to die cutting and cut into a waste material unit and a plurality of lined-up single label units, and the waste material unit and each label unit are respectively connected by multiple sections of reserved or adhered connecting lines to form an entangled structure. The method comprises the following steps that the label is firstly pressed and pasted on a pasted object by a thermoprinting machine or a thermal transfer printer or a heat sealing machine; then, the waste material unit is pulled; the connecting lines are broken or stripped; and the label units are separated from the waste material unit so as to finish the label pasting working procedure. According to the invention, material waste can be reduced, and cost is reduced. According to the invention, the automatic label pasting speed can be improved by the traditional general equipment, and the limitation of a rectangular label can be broken through.

A method of producing self-adhesive labels carried on a release backing material is described. This method provides a plurality of self-adhesive carrier labels across the width of a web of carrier substrate with at least one discrete label components, such as a leaflets, booklets, holograms and security devices, being applied to each respective carrier label. The invention provides a plurality of separate label component applicators across the width of the web, one for each respective label component to be applied. There is also provided a registration means to ensure registration of each label component with its respective carrier label. In this way the method provides for the manufacture of multi-layered combinations of individual labels to a base carrier using separate and specific component applicators for each label across the width of the web.

The invention relates to an ML-kNN (machine learning-k-nearest neighbor) improving method and an ML-kNN improving system applicable to multi-label classification. The ML-kNN improving method includes counting the sum of samples of each class of labels in original data sets, utilizing the sum of the samples of each class of labels in the original data sets as a label sample number, counting the sum of samples in each class of features in the samples of each class of labels, utilizing the sum of the samples of each class of features in the samples of each class of labels as a feature sample number and computing feature label weights according to the label sample numbers and the feature sample numbers; splitting each sample in the initial data sets into a plurality of original single-label samples with single labels, and updating feature values of each original single-label sample according to the feature label weights to generate first data sets; acquiring to-be-measured samples to be predicted, splitting the to-be-measured samples into to-be-measured single-label samples with single labels, sequentially predicting the labels of the to-be-measured single-label samples according to the first data sets and determining label sets of the to-be-measured samples. Each feature corresponds to the single corresponding feature value. The ML-kNN improving method and the ML-kNN improving system have the advantage that accurate prediction results of the samples in the aspect of multi-label classification can be obtained.