Abstract: An encoding method, a decoding method, and an apparatus are provided, and are applied to support a next-generation Wi-Fi protocol (for example, 802.11be, Wi-Fi 7, or EHT) of IEEE 802.11ax, a next-generation Wi-Fi protocol (for example, 802.11bn, Wi-Fi 8, or UHR) of IEEE 802.11be, Wi-Fi AI, millimeter wave (MMW), ultra-wideband (UWB), sensing, or the like. The method includes: A first communication apparatus obtains an information bit sequence, performs LDPC encoding on the information bit sequence based on a parity check matrix, to obtain an encoded sequence, and outputs the sequence. A length of the sequence is N1, where N1 is n times 1944, and n is an integer greater than or equal to 2. After obtaining to-be-decoded information, a second communication apparatus performs LDPC decoding based on the parity check matrix, to obtain the information bit sequence. The method can improve decoding performance.

Abstract: Example implementations described herein are directed to management of a system comprising a plurality of apparatuses providing unlabeled sensor data, which can involve executing feature extraction on the unlabeled sensor data to generate a plurality of features; executing failure detection by processing the plurality of features with a failure detection model to generate failure detection labels, the failure detection model generated from a machine learning framework that applies supervised machine learning on unsupervised machine learning models generated from unsupervised machine learning; and providing extracted features and the failure detection label to a failure prediction model to generate failure prediction and a sequence of features.

Abstract: A low-density parity-check (LDPC) encoding method includes performing LDPC encoding on an information bit sequence based on a parity check matrix, to obtain a first code word. The parity-check matrix is obtained by expanding a base matrix or a submatrix of the base matrix. The base matrix or submatrix is an H×M submatrix formed from H selected rows and M selected columns of a 12×22 matrix, where H is 1 to 12 and M is 1 to 22. The H×M submatrix includes two columns corresponding to first two columns of the 12×22 matrix and including predetermined binary sequences.

Abstract: The disclosure discloses a method for high-value application of PTA residue high-concentration bromine-containing wastewater to preparation of cuprous bromide, belongs to the field of PTA residue treatment, and includes: firstly adjusting a pH value of the bromine-containing wastewater to 0.5-2, adding cuprous oxide in batches, taking a reaction for 3-20 min after the cuprous oxide is totally added to produce cuprous bromide, Solid liquid separation to obtain cuprous bromide, concentrating a liquid phase to recover inorganic salt while rest wastewater may be used as process water for application. By cuprous oxide addition and method regulation and control, a bromine removal rate is as high as 95% or higher, the cuprous bromide reaches yield of 90% or higher and purity of 95%. Meanwhile, sodium sulfate with the purity of 90% or higher may be obtained. High-value application of the PTA residue high-concentration bromine-containing wastewater is really realized.

Abstract: Embodiments of the present application relates to the technical field of communication, and in particular to a user pairing method and apparatus, an electronic device, and a storage medium. The user pairing method includes: obtaining beam domain rasters to which users in a set of paired users belong, and a target beam domain raster to which a user to be paired belongs, wherein the beam domain rasters to which the paired users belong are different from the target beam domain raster; obtaining correlations between the target beam domain raster and the beam domain rasters respectively; and in response to that the correlations satisfy a preset condition, adding the user to be paired into the set of paired users.

Abstract: A positioning device for assembling a power tool holder housing and a use method of the positioning device, includes the power tool holder housing, a positioning table and elastic key blocks A; a lower cross keyway is formed in the bottom surface of the power tool holder housing, an upper cross keyway is formed in the upper surface of the positioning table; the lower cross keyway and the upper cross keyway are disposed in up-down correspondence; the elastic key blocks A are respectively mounted in corresponding keyways in four directions; an elongated circular notch is formed in the upper portion of a side surface of the elastic key block body A in the length direction of the elastic key block body, a cylindrical pin is disposed in the elongated circular notch, an opening of the elongated circular notch is located in the lower cross keyway.

Abstract: Disclosed in the present disclosure is a battery life early warning platform for a mobile energy storage apparatus based on battery thermal runaway analysis, relating to battery safety-related fields. The platform includes: a battery thermal runaway parameter set generation device configured to retrieve a historical battery thermal runaway log and generate a battery thermal runaway parameter set; an apparatus battery operating parameter acquisition device configured to perform real-time monitoring and acquire an apparatus battery operating parameter; a parameter comparison device configured to perform comparison and generate a parameter comparison result; a battery life early warning model building device is configured to build a battery life early warning model; an early warning signal output device is configured to acquire a battery state parameter and output an early warning signal; and an intelligent alarm device is configured to activate an alarm device and connect a remote terminal to perform alarming.

Abstract: Example implementations described herein can be directed to detecting a human disturbance from sensor data streamed from one or more sensors in a network of sensors; processing the detected human disturbance to determine a probability of a chainsaw event (e.g., light chainsaw or dense chainsaw event) and an estimated lead time for the chainsaw event for an area associated with the one or more sensors; and determining, for neighboring sensors to the one or more sensors in the network of sensors, a probability of a change of state to the human disturbance or the chainsaw event for other areas associated with the neighboring sensors.

Abstract: A method for virtual resource allocation includes: acquiring a first virtual resource, the first virtual resource being paid to an account of an application store when a user equipment acquires an authorization permission for a second application in the application store; the second application and a first application in the application store having an associated relationship, the second application being capable of calling the first application to implement functions of the first application; determining, based on a value of the second application and a value of the first application, a second virtual resource allocated to an account of the second application and a third virtual resource allocated to an account of the first application from the first virtual resource; and allocating the second virtual resource to the account of the second application and allocating the third virtual resource to the account of the first application.

Abstract: The present application relates to the field of communications, and discloses a data processing method and apparatus, and a terminal. The data processing method in embodiments of the present application comprises: performing spatial decomposition on received data of a plurality of receiving antennas to obtain a plurality of sub-spaces; measuring the signal quality of at least one sub-space; selecting a target sub-space from the plurality of sub-spaces according to the signal quality of the sub-space; and processing the received data by means of the target sub-space.

Abstract: A method for characterizing an effective dielectric constant of a substrate including glass fibers, including: constructing a real model, where the real model includes: the substrate, a glass fiber-woven layer on the substrate, and a transmission line trace on the substrate; performing a phase shift simulation on the real model according to phase shift value; and obtaining first information; constructing a simplified model, where the simplified model includes: the substrate, and the transmission line trace on the substrate; setting a dielectric constant of the substrate as a variable; performing parameter scanning simulation; and obtaining a second information; and according to the first and second information, determining a dielectric constant value when the phase shift value of the simplified model equals the phase shift value of the real model, and using the dielectric constant value as the effective dielectric constant of the substrate including the glass fibers.

Abstract: Provided is a method and system for intelligent segmentation of fundus blood vessels based on retinal angiography. The method includes: acquiring fundus images with retinal angiography technology; performing a preprocessing operation on the fundus images to obtain preprocessed images; performing a superpixel segmentation on the preprocessed images to obtain segmented areas; performing a fundus blood vessel recognition on the segmented areas to obtain a recognition result; and constructing a trained fundus blood vessel segmentation model, and performing a feature segmentation on the recognition result by using the fundus blood vessel segmentation model to obtain a segmentation result. This method performs a multi-level feature extraction and fusion through the intelligent segmentation model, realizes effective extraction of multi-dimensional features of the fundus images, such as color, texture, shape and intensity, and improves segmentation accuracy through the feature fusion.

Abstract: In example implementations described herein, there are systems and methods for training a machine-trained model for industrial failure detection based on an initial set of training data comprising a plurality of input-output data sets each comprising input data and output data regarding at least one classification of the input data. The method may include identifying at least one classification of the input data, in a plurality of classifications that is under-represented. The method may also include automatically generating, for inclusion in a modified set of training data, additional input-output data sets for the identified at least, one classification to balance a representation of the classifications in the modified set of training data. Finally, the method may include training the machine-trained model based on the modified set of training data comprising at least a subset of the initial set of training data and the additional input-output data sets.

Abstract: Provided is an automatic calculation method for a surface defect area of a wet blue skin based on a DeeplabV3+ model, aiming at solving the problem that the automatic calculation of the surface defect area is difficult in a tannery. The method includes image data acquisition and preprocessing of a wet blue skin, training and verification of a wet blue skin surface defect segmentation model based on a DeeplabV3+ model, and automatic calculation of the surface defect area in a segmented image. The method can accurately segment common defects such as knife holes, brands and the like on the surface of a large-area wet blue skin, with a high speed of detecting a wet blue skin within 1 s.

Abstract: The present disclosure provides a model compiling method. The method includes that: a target computation sub-graph in a computation graph corresponding to a model to be compiled is obtained and the target computation sub-graph includes multiple memory-access-intensive operators and data corresponding to the multiple memory-access-intensive operators; multiple groups of computation tasks to be serially executed by a target thread are determined based on multiple threads set in an executor of the model and a computation task parallelism degree of each thread sequential rearrangement is performed on multiple computation tasks contained in the any group of computation tasks, and an execution instruction sequence both corresponding to the target thread and the any group of computation tasks is generated; and an executable program file for executing the model by the executor is determined based on the execution instruction sequence corresponding to the any group of computation tasks.

Abstract: A method for generating persona specific insights. The method may include receiving sensor data associated with a device; extracting features from the received sensor data; processing the features using a machine learning model to generate machine learning metrics; ingesting the machine learning metrics and the features to generate insights data associated with the device; generating personas data using the insights data and the features, and mapping the insights to the personas data; generating custom insights using the insights data, the personas data, and the features, wherein the custom insights are text-based summaries; and disseminating each of the custom insights to respective persona of the personas data to place service orders associated with the device.

Abstract: Systems and methods described herein involve generating an asset hierarchy from a plurality of images of a plurality of asset, which can involve executing a machine learning process to identify the plurality of assets from the plurality of images; identifying relationships between the identified plurality of assets based on feature extraction and analysis of extracted features of the identified plurality of assets; and generating the asset hierarchy from one or more asset hierarchy templates derived from the identified relationships between the identified plurality of assets.