81 results about "Preprocessor" patented technology

The invention discloses a power plant operation and maintenance knowledge intelligent query method based on a large language model and an RAG technology. The method comprises the following steps: constructing a power plant operation and maintenance knowledge vector library covering structured, semi-structured and unstructured data; receiving a natural language question of a user, inputting an improved instruction to align a preprocessor, and generating a question semantic vector and an intention tag; relevant knowledge fragments are retrieved and sorted through a semantic matching retriever in combination with the intention labels; constructing a large language model cue word structure based on the retrieval result and the original question, generating candidate answers and recording a reference path; and finally, performing term specification and consistency verification according to the expert rule base, and outputting a structured and traceable final answer. According to the invention, the improved RAG technology is fused to realize intelligent query of the operation and maintenance knowledge of the power plant.

A deep learning system for time series prediction comprising a preprocessor that receives time series input sequences, truncates them by removing terminal values, and appends padding values to maintain the original sequence length. An encoder compresses these padded sequences into latent space representations, while a decoder reconstructs predicted sequences matching the original length, specifically trained to reconstruct values matching the removed terminal values in positions corresponding to the padding values. A training system optimizes the encoder and decoder by minimizing differences between original sequences and predicted sequences. The system can process multiple time horizons simultaneously while maintaining statistical properties and providing uncertainty quantification through confidence intervals. This approach enables accurate short-term forecasting while preserving both temporal patterns and statistical relationships in the predicted sequences.

An adaptive data system (ADS) for cognitive data processing is disclosed. The ADS includes an adaptive semantic preprocessor, a trigger detector, a temporal batching engine, a symbolic encoder, and a dynamic cognitive transformer engine. The adaptive semantic preprocessor is configured to receive input data from one or more databases and identify cognitive data attributes comprising one or more contextual, semantic, and temporal attributes from the received input data. The trigger detector is configured to identify semantic divergence of the identified cognitive data attributes and provide a standardized data. The temporal batching engine is configured to provide a high-dimensional cognitive data from the standardized data. The symbolic encoder compresses the high-dimensional cognitive data. The dynamic cognitive transformer engine is configured to determine decision making rules, analyze the compressed high-dimensional cognitive data based on the decision making rules and provide recommendations based on an outcome of the analysis to a user.

In one embodiment, a method includes receiving data of a set of configurations of preprocessor engines, receiving measurements of performance of a device executing benchmark applications while changing a configuration of preprocessor engines selected from the set of configurations of preprocessor engines, defining an order of at least some of the configurations based on the measurements, and providing a pruned set of configurations based on the defined order of the at least some configurations.

A method and system for analyzing a vehicle winch design includes a preprocessor determining contact locations for winch components from a mesh model. The preprocessor determines load vectors at a winch assembly and counter-vectors at a fairlead based on a relative position of a winch wire and a hook retention location based on a winch load rating. A non-linear analysis system determines plastic strains, deflections, and clearances under the winch load rating based on the contact locations, load vector and counter vector, and communicates the plastic strains, deflections, and clearances to a post processing system. The post processing system compares the plastic strains to strain limit at a post processor, compares the deflection to a deflection limit, compares clearances to a clearance limit and generating a display based on comparing the plastic strains, compares the deflection to the deflection limit and compares clearances to the clearance limit.

The invention relates to the technical field of artificial intelligence, and discloses a separated multi-mode large language model service system and a first lexical element generation method. The system comprises a preprocessor, an encoder instance, an instance interaction layer and a pre-filling instance, the preprocessor is configured to analyze the question and answer request to obtain original data of multiple modals; the encoder instance is used for carrying out parallel encoding on the original data of each mode to generate a subsequence of the corresponding mode; the instance interaction layer is configured to send a sub-sequence of each mode generated by the encoder instance to the pre-filling instance; the pre-filling instance comprises a large language model trunk and is configured to perform asynchronous pre-filling on each sub-sequence of each mode by taking the sub-sequence as granularity so as to generate a first lexical element of an answer text corresponding to the question and answer request. By adopting the system, the response speed of the online question and answer service system can be improved, and the user experience is improved.

The invention discloses an epileptic seizure detection system based on a multi-dimensional hypergraph fusion network, which comprises a multi-modal data collector, a signal preprocessor, a feature extractor, a multi-dimensional hypergraph builder, a hypergraph feature extractor, a feature fusion device, a classifier and a time sequence prediction corrector, the multi-dimensional hypergraph constructor constructs three hypergraph structures, namely an intra-modal hypergraph, an inter-modal hypergraph and a time sequence hypergraph; the feature fusion device integrates the features extracted by the three hypergraphs to generate a comprehensive feature vector; the classifier classifies the fusion features through a two-layer feedforward neural network, and outputs probability prediction of epileptic seizure; the time sequence prediction corrector applies a majority voting smoothing filter to carry out post-processing on a classification result, isolated misclassification is eliminated, and the time sequence consistency of prediction is enhanced. The invention provides an innovative multi-dimensional hypergraph fusion network, and the robustness and accuracy of epileptic seizure detection are remarkably improved by simultaneously modeling high-order relationships in modals, between modals and time sequence dimensions.

The invention discloses a multi-source heterogeneous big data driven intelligent contract abnormal behavior real-time detection and early warning method, and relates to the technical field of block chains. According to the invention, a multilayer data preprocessor is adopted to carry out standardized conversion on intelligent contract codes, transaction records and network traffic, the problem that the traditional method is limited to single data source analysis so that the detection accuracy is low is solved, and a graph neural network, a time sequence analysis algorithm and a deep auto-encoder are utilized to carry out deep analysis on data. Multi-dimensional features of abnormal behaviors are comprehensively captured, the detection accuracy is remarkably improved, false alarm and missing alarm are effectively reduced, real-time data streams are processed by means of a streaming computing framework, millisecond-level real-time detection, quick response and early warning of the abnormal behaviors are achieved through parallel feature extraction and distributed computing resource scheduling, and safe operation of smart contracts is ensured.

The invention relates to the technical field of ion beam physical calculation, in particular to an ion beam optical property evaluation method based on a CPU-GPU hybrid parallel stable double-gradient conjugate algorithm. According to the technical scheme, the method comprises the following steps that a CPU-GPU hybrid parallel architecture is initialized, and CPU end and GPU end memory allocation, CUDA related object creation and GPU equipment initialization and parameter setting are completed; constructing a linear equation set, a sparse coefficient matrix A and a right-end vector b, and setting an initial solution vector, convergence precision epsilon and related parameters of the number of iterations; and analyzing the sparseness of the coefficient matrix A. Through combination of three core mechanisms of CPU-GPU cooperative calculation, dynamic load balancing and intelligent preprocessor selection, efficient, stable and universal evaluation of the ion beam optical characteristics is successfully realized, and an excellent solution is provided for solving the solving problem of a large-scale sparse linear equation set in the field of high-performance calculation.

The present invention provides an industrial equipment fault diagnosis method, system, and storage medium based on multi-view expansion statistical features, which relate to the technical field of industrial equipment fault diagnosis. The method aims to solve the problems that existing methods are easily contaminated by noise, and that the extracted features contain redundant and irrelevant features, resulting in long calculation time, low calculation efficiency, and high hardware computing power requirements. The method comprises the following steps: step 1, obtaining a time series data set of the operating fault status of the industrial equipment; step 2, preprocessing the time series data using an unsupervised differential expansion mapping preprocessor; step 3, constructing multiple filtering feature selectors at different storage ratios to obtain multi-view feature subset vectors and a combined feature selector; step 4, constructing multiple classifiers, training the multiple classifiers based on the multi-view feature subsets, and constructing a combined classifier; and step 5, using the combined feature selector and the combined classifier to perform fault diagnosis on the equipment operating status.

An activity classifier system and method that classifies human activities using 2D skeleton data. The system includes a skeleton preprocessor that transforms the 2D skeleton data into transformed skeleton data, the transformed skeleton data comprising scaled, relative joint positions and relative joint velocities. The system also includes a gesture classifier comprising a first recurrent neural network that receives the transformed skeleton data, and is trained to identify the most probable of a plurality of gestures. The system also has an action classifier comprising a second recurrent neural network that receives information from the first recurrent neural networks and is trained to identify the most probable of a plurality of actions.

A method for processing an input frame for an on-device AI model is provided. The method may include obtaining an input frame. The method may include building at least one kernel independent of the scale of the input frame by passing input variables to the at least one kernel using preprocessor directives independent of the scale of the input frame. The method may include inputting the input frame to the on-device AI model including the at least one kernel independent of the scale of the input frame. The method may include processing the input frame in the on-device AI model.

An audio encoder for encoding audio input data has: a preprocessor for preprocessing the audio input data to obtain audio data to be coded; a coder processor for coding the audio data to be coded; and a controller for controlling the coder processor so that, depending on a first signal characteristic of a first frame of the audio data to be coded, a number of audio data items of the audio data to be coded by the coder processor for the first frame is reduced compared to a second signal characteristic of a second frame, and a first number of information units used for coding the reduced number of audio data items for the first frame is stronger enhanced compared to a second number of information units for the second frame.

Embodiments herein provide a method and an electronic device for compressing video for an AI-based in-loop filter (AILF). The method includes obtaining a video including a plurality of frames, where each of the plurality of frames includes a plurality of channels. Further, the method includes extracting at least one feature from each of the plurality of frames of the video. Further, the method comprises selecting at least one preprocessor from the plurality of preprocessors (311) for the AILF (318) based on the at least one feature from each of the plurality of frames. Further, the method includes generating an encoded video by encoding image information for each of the plurality of channels from each of the plurality of frames using the selected at least one preprocessor.

A determination system includes: a log obtainer that obtains log information; a preprocessor that generates preprocessed log information from the log information; a primary analyzer that generates primary determination result information by performing primary analysis on the preprocessed log information; a prompt generator that generates a prompt for generative artificial intelligence (AI) based on the preprocessed log information; a secondary analyzer that performs, as secondary analysis, a process of obtaining secondary determination result information by inputting the prompt to the generative AI; an overall determiner that generates overall determination result information based on the primary determination result information and the secondary determination result information; and an outputter that outputs a determination result report that includes a determination result indicated by the overall determination result information.

The present disclosure relates to a video stream processing method and device, electronic equipment and storage medium. The method can include: transmitting a video stream at a current time to a preprocessor to cause the preprocessor to perform road element recognition on the video stream at the current time to obtain an element recognition result corresponding to the current time; sending the element recognition result corresponding to the current time to a main processor to cause the main processor to predict a video stream at a next time based on the element recognition result corresponding to the current time, determining first target allocation information in a case where a video stream prediction result represents that the predicted video stream at the next time is a first type of video stream, and awakening a coprocessor in a case where the coprocessor is not awakened. According to the technical scheme provided by the present disclosure, the video stream at the next time can be predicted, and in a case where it is determined that the video stream at the next time is the first type of video stream, the coprocessor is awakened in advance to improve the response timeliness of the coprocessor, i.e., the timeliness of processing data.

This invention provides an automatic identification method and system for intersection features based on manufacturability analysis, comprising: Step 1: Based on the swing range of the five-axis machine tool axis and the tilt angle range of different machining methods, and according to the definition of manufacturability and the classification of machining surface types, the machining method of each surface is obtained, and the end face is identified by combining the tool reachability geometry algorithm; Step 2: Using the end face as the reference surface, a segmentation surface is constructed by extending the reference surface; Step 3: The difference between the preprocessor and postprocessor is calculated to obtain the machining domain of the process, and the machining area is obtained by finding the intersection of the segmentation surface and the machining domain; Step 4: A domain unit tree is constructed through the dependency relationship between domain units, and domain units with the same dependency surface are merged to construct intersecting domains, thus completing the identification of intersection features. This invention effectively solves the problem that volume decomposition algorithms are not applicable to complex surface features.

An apparatus for diagnosing an autism spectrum disorder (ASD) based on a graph neural network includes a preprocessor configured to acquire brain image data, designate a region of interest (ROI) of the brain, and generate preprocessed data for neural network input, a spatial feature extractor configured to extract spatial features from the preprocessed data, a temporal feature extractor configured to analyze changes in brain activity over time and extract attention-based temporal features, a spatial and temporal convergence feature unit configured to analyze spatiotemporal correlations by combining the spatial features and the temporal features, a graph generator configured to convert connectivity between regions of interest into a graph structure and implement the same as nodes and edges, and a graph classifier configured to analyze a spatiotemporal pattern of the connectivity through the graph structure and classify whether or not there is an ASD.

In an object arrangement apparatus and method, the object arrangement apparatus includes a data manager configured to manage object data including information on identifiers, sizes and an arrangement order of objects, and set box data including information on types and sizes of set boxes, a data preprocessor configured to determine a type of a set box in which each object is arrangeable, based on the object data and the set box data, and perform preprocessing on the object data, and an object arranger configured to determine a set box in which an object is arranged depending on the arrangement order of the objects, based on the preprocessed object data and the set box data, and perform arrangement of the object in the determined set box.

The invention discloses an intelligent data fusion and health early warning system and method for a multi-level health and care institution, belongs to the technical field of health and care analysis, and realizes unified modeling of multi-source heterogeneous data such as medical texts, time sequence data and images through a hierarchical heterogeneous graph neural network and a dynamic edge weight mechanism. The problem of health assessment fragmentation caused by non-standardization of data in the field of pension is solved, a space-time enhancement graph structure is introduced, a health state evolution rule is accurately captured through time attenuation factors and medical knowledge constraints, a special preprocessor is designed for special diseases, a filtering threshold value is dynamically adjusted through tremor scores, and the health assessment accuracy is improved. The problem of noise confusion is effectively solved, and the health prediction result has clinical traceability based on label system construction and graph structure visualization of the medical knowledge graph.

This invention relates to a wide-scale image input / output processing method for a road collapse hazard detector, belonging to the field of image processing technology. It utilizes a sliding window preprocessor with multiple widths and step sizes to repeatedly crop wide-scale ground-penetrating radar (GPR) images, forming multiple sets of input images with conventional ratios. These input images are then fed into a pre-trained target detector for road collapse hazard identification. This technique not only solves the problem of wide-scale input images but also, due to the extensive and repeated cropping of the wide-scale input images, aligns with the principle of test data augmentation, thereby improving the hazard identification rate. The road collapse hazard identification results in each cropped image are mapped back to the original wide-scale GPR image. Due to repeated cropping, the target boxes of road collapse hazards mapped to the original image may overlap. This method sorts the overlapping boxes in descending order of confidence level and removes redundancy based on the degree of overlap, confidence level, and individual coordinates to obtain the final detection result.

A semantic data processing system for processing an unstructured data file based on semantics of textual content of the unstructured data file comprises a processing arrangement configured to: receive, structured reference data, via reference data ingester; receive unstructured data files via an unstructured data file preprocessor: generate, via knowledge graph generator, knowledge graph from the structured reference data using structure of the structured reference data, the knowledge graph specifying a plurality of different entities having relationships with each other and relationship data linking the different entities; generate, via entity-centric search query generator, entity-centric search query by using the knowledge graph, the entity-centric search query specifying plurality of entity-related textual terms and parameters regarding the relationships between the entity-related textual terms; and execute, via contextual matching engine, the entity-centric search query on the received unstructured data file to generate a match score relating to the relevance of textual content of the unstructured data file to the entity-centric search query.

An apparatus for a DNN operation includes a preprocessor configured to classify outlier data and inlier data from a predetermined number of pieces of grouped and input floating-point data and to perform presorting on the inlier data, a CIM operator configured to perform a fixed-point operation on the inlier data, an NPU operator configured to receive the outlier data and corresponding input channel information from the preprocessor and to perform a floating-point operation on the outlier data, and an aggregation core configured to sum and output an operation result of each of the CIM operator and the NPU operator, wherein the NPU operator reads a weight for each input channel for the floating-point operation on the outlier data through a separate transmission line implemented in the CIM operator, and causes the outlier data to be processed in parallel with an operation cycle of the inlier data.

A Contrastive Forecasting Explanation (CFE) tool and technique provides a model-agnostic approach to forecasting explanation. The CFE tool uses an ML-based surrogate forecaster as a surrogate model. The surrogate forecaster includes a time series preprocessor, a simple concept generator, and an ML forecaster. The subsequent interpretation of the predictions of the time series forecaster is based on the behavior of the surrogate forecaster. The CFE tool interprets time series forecasts by identifying the specific temporal concepts impacting predictions and thus generates clear and reliable explanations regardless of model type. The simple concepts and predictions generated by the surrogate model are input into a perturbation-based explainer to produce feature attributions from the surrogate model. An attribution postprocessor aggregates the attributions into more coherent concepts to present a coherent, concise, and interpretable explanation.

This invention discloses a sleep staging system based on multi-threshold neighborhood extreme value pattern statistics, comprising an EEG signal acquisition unit, a signal preprocessor, a multi-threshold neighborhood extreme value time-domain feature extractor, a gray wolf optimizer, and a random forest classifier. The preprocessing involves initial filtering and 30-second segmentation of selected EEG signals. Based on the duration of the single-channel EEG signal annotation, the single-channel EEG signals are divided into two categories: original signal segments and signal segments requiring data augmentation. Next, data augmentation is performed on specified segments, and the extreme values ​​of the augmented data segments are selected. Then, the neighborhood differences of the extreme values ​​are calculated, and multi-threshold and multi-state processing is performed on the 5-stage matrix obtained from these differences to derive the multi-threshold neighborhood extreme value pattern statistical code. The thresholds in the multi-threshold processing are determined by the gray wolf optimizer. Finally, the obtained feature matrix is ​​input into the random forest classifier. This invention enhances the automatic sleep staging capability based on EEG signal augmentation and designs and manufactures a complete system encompassing EEG acquisition and sleep analysis visualization.

A system and a method for providing a multi-functional framework to perform one or more user-specified tasks is disclosed. The system includes a user profile generating subsystem, a data obtaining subsystem, an authentication subsystem, an access control subsystem, and an application programming interface (API) subsystem. Users are authenticated using biometric data, credentials, and metadata before being granted controlled access to in-platform applications such as document management, customer relationship management (CRM), financial operations, and content publishing. The system further integrates third-party applications and tools using API calls initiated via a hypertext preprocessor (PHP) procedure. An artificial intelligence (AI) automation subsystem executes user tasks across integrated environments, while a blockchain activity logging subsystem tracks transactions and rewards users with blockchain-based crypto tokens. The blockchain-based crypto tokens are able to be redeemed for the system services or digital assets.

A server to manage a plurality of robots, where each robot performs a specified set of tasks, includes a robot management table, an operational database, a preprocessor and an initializer. The robot management table lists per robot tasks and associated scripts according to a unique robot ID for each of the plurality of robots. The operational database stores operational data required to run the scripts. The preprocessor runs at least one algorithm to obtain and update the operational data. The initializer initializes a particular robot of the plurality of robots according to its unique robot ID with its associated the scripts when the server is in network communication with the particular robot.

The invention provides a data exchange visual arrangement system and method based on an API, and relates to the technical field of API data processing and flow arrangement, and the system comprises a component library which is used for storing a plurality of reusable visual components, and each visual component corresponds to a conventional operation node in a third-party platform docking process; the arrangement grouping module is used for creating arrangement groups according to API arrangement requirements associated with functions and configuring a preprocessor and a postprocessor for the arrangement groups; the visual composer is used for associating each visual component with the corresponding preprocessor and the corresponding postprocessor according to the required visual component dragged from the component library by a developer and the quoted created composing group; and according to an execution sequence and a data flow direction of each visual component defined by a developer through a connection line, completing an API arrangement process and generating a data exchange process graph. The method has the beneficial effects that the third-party docking efficiency is greatly improved, and the docking threshold is greatly reduced.

Techniques described herein relate to systems and methods for parallel DNA molecules sequencing. A preprocessor can receive raw data frames from a sensor chip including 100,000 or more cells, where each raw data frame can include detection signals from the 100,000 or more cells at a given time during the formation of the 100,000 or more cells or during the DNA molecules sequencing using the 100,000 or more cells. The preprocessor can then extract relevant information for determining states of the cells from the raw data frames, generate one or more digested frames that includes the extracted information, and send the digested frames to a processor for processing, such as base determination. Because the number of digested frames sent to the processor is less than a number of the raw data frames and the digested frames include preprocessed data, the amount of data being transferred to the processor and the amount of data processing by the processor can be reduced.