152 results about "Sequential data" patented technology

The Marker Fingerprinting system provides a method for identifying and correlating serial or sequential data patterns across diverse domains such as geological, biological, and financial datasets. This innovation transforms single- or multi-attribute data series into feature matrices, generating unique hash tokens—or fingerprints—that encapsulate specific data patterns. Using advanced signal analysis and spectral transformations, it enables efficient processing and pattern recognition within complex datasets. Fingerprints from reference patterns are matched against target datasets, with quantitative confidence metrics derived from weighted algorithms assessing match accuracy. Iterative data conditioning enhances robustness by addressing noise and inconsistencies, ensuring reliability at scale. The invention improves decision-making by delivering rapid and accurate pattern identification with quantified reliability, making it particularly suited for applications like geological top picking, seismic data analysis, and other fields requiring precise data correlation

The invention belongs to the technical field of intelligent manufacturing, and particularly relates to a manufacturing quality prediction method and system based on a multi-mode sequential network and application, and the method comprises the steps: carrying out the preprocessing of the sequential data of a process manufacturing production line, obtaining a sample set, and carrying out the sequential division into a training set, a verification set and a test set; on the basis of the sample set, key features are enhanced through a frequency domain enhanced channel attention mechanism, a multi-period mode of a time sequence dependence and period sensing module is captured in combination with a multi-layer expansion convolutional network structure, and a multi-mode time sequence network model is constructed; and sequentially carrying out training set training, verification set parameter adjustment optimization and test set performance verification on the multi-modal sequential network model, and outputting a prediction result. According to the method, the deep dynamic association among the multivariable time series data can be mined, the accuracy and robustness of manufacturing quality prediction are improved, and an efficient and reliable technical scheme and an implementation path are provided for process industry quality control and intelligent optimization.

The invention relates to a computer-implemented method for determining features of the interior (1) of a vehicle (2) with at least one image capturing unit (3), a data processing unit (4) and a database (5), comprising the capturing, by the image capturing unit (3), of a photo (11) or video of the interior (1) and transmission to the data processing unit (4), the analysis of the photo (11) or video in the data processing unit (4) by a sequential data processing chain (10) of program modules (12, 12′, 12″), comprising the steps of rough analysis for the localisation of persons and objects and generation of body and object images (15, 15′, 15″), detailed analysis of the body and object images (15, 15′, 15″), extraction of body and object data (7), and storage, by the data processing unit (4), of the determined body and object data (7) in a hierarchical data model (9) in the database (5).

A method includes obtaining first feature vectors and second feature vectors representing contents of a first and second image frame, respectively, of an input video. The method may also include generating, based on the first feature vectors, first slot vectors, where each slot vector represents attributes of a corresponding entity as represented in the first image frame, and generating, based on the first slot vectors, predicted slot vectors including a corresponding predicted slot vector that represents a transition of the attributes of the corresponding entity from the first to the second image frame. The method may additionally include generating, based on the predicted slot vectors and the second feature vectors, second slot vectors including a corresponding slot vector that represents the attributes of the corresponding entity as represented in the second image frame, and determining an output based on the predicted slot vectors or the second slot vectors.

One or more systems, devices, computer program products and / or computer-implemented methods of use provided herein relate to TQFMs for kernel-based sequential data prediction. A system can comprise a memory that can store computer-executable components. The system can further comprise a processor that can execute the computer-executable components stored in the memory, wherein the computer-executable components can comprise a computation component that can use a TQFM to compute a kernel element between two sequences of symbols, on a quantum computer, by respectively processing two input sequences as vectors.

The invention discloses a two-stage checking method for the node water demand and the pipe section friction resistance coefficient of a water supply pipe network, and is applied to the technical field of real-time online correction of a water supply pipe network model. Comprising the following steps: grouping node water demand and a pipe section friction resistance coefficient based on prior information, calculating a covariance matrix of pressure and flow measuring points, calculating estimated values and covariances of the node water demand and the pipe section friction resistance coefficient through a first-stage sequential data assimilation algorithm according to monitoring data at the current moment, and calculating the node water demand and the pipe section friction resistance coefficient through a second-stage sequential data assimilation algorithm; and then a calculation result serves as an empirical value to be added into a target function of second-stage iterative calculation, an estimated value serves as an initial value of an iterative algorithm to be subjected to fine tuning optimization, an optimal estimated value of parameters at the current moment and a covariance matrix of the optimal estimated value are obtained, the calculation result is brought into the next moment, and the two-stage algorithm is repeated to check model parameters at the next moment. The advantages of the two-stage algorithm complement each other, the checking precision of the parameters is improved, and a new thought is provided for real-time online correction of the water supply pipe network model parameters.

The invention discloses an abnormal Transform model-based electricity larceny detection method and device and a storage medium, and relates to the field of electric power system electricity larceny detection, and the abnormal Transform model-based electricity larceny detection method comprises the following steps: capturing complex correlation among different time points in sequence data by using a self-attention mechanism of a Transform model; the characteristics of the normal time point and the abnormal time point are effectively distinguished by learning the relevance difference of each time point, the accuracy of electricity stealing user identification is improved, and the misjudgment rate is reduced. The method comprises the following steps: S1, obtaining user multi-dimensional power consumption time series data, preprocessing the data, and constructing a sliding window sample; s2, constructing an electricity larceny detection model based on an Angle-Attention mechanism, wherein the electricity larceny detection model comprises a prior correlation branch and a sequence correlation branch; s3, quantifying the time dependence deviation of the power consumption behavior by taking the relevance difference as an anomaly judgment standard; s4, training the model by using a minimax optimization strategy, and amplifying the relevance difference between normal electricity utilization and electricity stealing electricity utilization; and S5, performing electricity larceny risk prediction and model performance evaluation through the joint score of the relevance difference and the reconstruction error.

Computerized methods and systems obtain threat data generated from activity data using unsupervised learning. The activity data is collected from enterprises and describes activities performed on the enterprises. The threat data indicates likelihood that sequences of activities performed on the enterprises are indicative of malicious intent. A supervised ML model that processes sequential data is trained by providing a training set of sequential data to the supervised ML model. The training set includes at least some of the obtained threat data, and data derived from activity data collected from at least some of the enterprises. The trained supervised ML receives new data that describes a sequence of activities performed on an enterprise, and processes the received new data to produce a prediction of whether the sequence of activities performed on the enterprise will lead to a malicious action on the enterprise. In some embodiments, multiple supervised ML models are used.

A data storage device includes a bandwidth balancing system operable to reduce or eliminate bandwidth availability fluctuations that occur as a result of the performance of various internal operations and host operations. The bandwidth balancing system reduces or eliminates bandwidth availability fluctuations using a randomness factor. The randomness factor is a value that indicates a probability that an entire memory block will be invalidated by a single operation, which would cause the validity count of the memory block to significantly drop, thereby causing bandwidth availability fluctuations. The bandwidth balancing system ensures the memory blocks have a desired randomness factor by enabling the memory blocks to store both random data and sequential data. Specifically, the bandwidth balancing system intelligently mixes random data and sequential data within a memory block to achieve the desired randomness factor.

A computer-implemented method for anomaly detection for a time series data is provided. Aspects include receiving a time series data including a plurality of sequential data points, calculating an expected next value for the time series data based on the plurality of sequential data points, and receiving an actual next value corresponding to the time series data. Aspects also include calculating an anomaly strength estimate based on the expected next value and the actual next value, identifying one of a plurality of anomaly detection pipelines based on the anomaly strength estimate and a portrait associated with each of the plurality of anomaly detection pipelines, and obtaining an anomaly prediction by inputting the time series data and the actual next value into the one of the plurality of anomaly detection pipelines.

Methods, systems, and devices for write buffer management for a memory system are described. The described techniques provide for a memory system to receive data associated with multiple applications being executed concurrently and store the data to portions of a write buffer according to whether the data is sequential or non-sequential. For example, the memory system may receive sequential data for a first application between receiving non-sequential data for one or more second applications, and may partition a write buffer such that the sequential data is stored (e.g., sequentially) within a portion the write buffer and the non-sequential data is stored within a different portion of the write buffer. The memory system may flush portions of the write buffer to multiple-level memory cells once a portion is full, thereby storing sequential data to sequential physical addresses within the memory system.

A system and method for robotic teleoperation enable a teleoperated robotic element to perform a sequence of actions based on intention estimation, eliminating the need for continuous human control. The system includes a robotic teleoperation input that receives motion inputs and gaze data from a human operator performing a robotic teleoperation task. A robotic teleoperation feature extractor analyzes and processes the motion inputs and the gaze data into sequential input data. A multi-window model assigns hierarchical prediction windows to the input data, generating windowed sequential data. A hierarchical neural network processes the windowed sequential data to determine low-level action intentions and high-level task intentions, and generates an intention estimation based on the low-level action intentions and the high-level task intentions. A hierarchical dependency model incorporates hierarchical dependent loss to refine the intention estimation.

The invention discloses a multi-source information fused intelligent inspection system, method and equipment with a target guiding function. The system comprises an inspection management platform, a data base station, a wireless routing base station, a quadruped robot, a pose sensor and a multi-mode sensor unit, wherein the inspection management platform, the data base station, the wireless routing base station, the quadruped robot, the pose sensor and the multi-mode sensor unit comprise a laser radar, a visible light camera and an infrared thermal imager; a control processing terminal is arranged on the quadruped robot and is used for acquiring data acquired by the multi-mode sensor unit and the pose sensor, processing the acquired data and outputting a control signal according to external instruction data, so that the quadruped robot moves according to an inspection planning path; the multi-mode sensor unit and the pose sensor are controlled to work; and the inspection management platform is used for analyzing and processing the data acquired and processed by the control processing terminal, and outputting instruction data including an inspection planning path to the control processing terminal. According to the invention, the inspection safety of engineering scenes in dangerous environments such as underground spaces and tailings ponds can be improved.

The invention provides a profile data set construction method and system based on multi-source heterogeneous ocean observation, and the method comprises the steps: obtaining multi-source heterogeneous original ocean observation profile data and description information of the data from a plurality of target ocean data centers / observation mechanisms in a target time period; performing version cleaning on the original ocean observation profile data according to the unique identifier determined by the metadata and the description information of the original ocean observation profile data, and performing high-frequency cleaning on the original ocean observation profile data based on the spatio-temporal joint features to obtain multiple pieces of target ocean observation profile data; and performing matrix processing on the target ocean observation profile data to obtain corresponding normalized data so as to construct a profile data set in the target time period. Therefore, the multi-source heterogeneous profile data can be subjected to data cleaning and format normalization in sequence, so that the consistency, accuracy and availability of the data are improved.

The invention discloses a wind turbine generator component life probability prediction method and system, and a wind power plant energy control method and system. The prediction method comprises the steps of constructing a state space model of a degradation process of a wind turbine generator component; acquiring real-time state information of the component; inputting the real-time state information into the state space model, evaluating the degradation state of the component by adopting a sequential data assimilation algorithm, and outputting posterior probability distribution of the degradation state; and based on the posterior probability distribution, forward simulating a future degradation path of the component by adopting Monte Carlo simulation until the future degradation path exceeds a preset failure threshold, and obtaining residual life probability distribution of the component. According to the method, the personalized residual life of each unit can be accurately evaluated, and an innovative wind power plant energy management control strategy is created on the basis, so that the absolute maximization of the full-life-cycle economic benefit of the wind power plant in a complex market environment is realized.

The invention provides a self-adaptive cardinality estimation method, system and device and a storage medium, and belongs to the field of database query optimization, and the method comprises the steps: constructing original connection sequence data and an optimal table sequence of a table based on a disclosed benchmark test data set as a data set, and converting the data in the data set into an original feature vector; the original feature vectors are input into a plurality of Transform models with different hyper-parameter settings for model training, a linear transformation module is included in front of each Transform model, and a plurality of cardinal number prediction results are obtained through training; and selecting a model with a minimum error as an optimal model according to a cardinality prediction result to obtain all original query feature vectors and corresponding optimal models, and learning a mapping relationship between the query features and the optimal models by utilizing a classifier. Obtaining a new SQL query input classifier, and distributing an optimal model based on the index data set; and processing the feature vector by using the optimal model to obtain a cardinality estimation result. The defect of single model query is changed, and the accuracy of a query result is improved based on feature classification query.

The invention discloses a high-concurrency and sequential data processing method for batch import class interfaces. In a request verification and forwarding stage, request data are verified, a guarantee sequence is locked based on a merchant ID after verification is passed, then the data are batched, request and batch information is persisted to a database, batch information is delivered to a corresponding message queue Topic according to a service scene, and the request ID is returned. In the consumption queue data stage, a consumer thread subscribes to messages of the message queue, processes the messages, updates a batch state, manually submits consumption offset, and dynamically adjusts the sleep duration of the thread according to a system load to realize intelligent peak clipping. And a progress result query stage: extracting data from the database based on the request ID, calculating a processing progress and a failure reason, formatting and returning the processing progress and the failure reason, and supporting front-end visual display. Through the message queue peak clipping, the differentiation sequence control strategy and the multi-dimensional data persistence, the high concurrency bearing capacity of the interface is improved, and the data consistency and reliability are guaranteed.

There is disclosed an apparatus (100, 200, 300, 400, 500) comprising: an input interface (101) to receive an input segment (102) of an input sequential data; a neural network, NN, processor (110) to derive an output result (104) by processing the input segment (102) through a NN having a number of layers (111) from a first layer to a last layer, the NN using a predetermined number of deactivatable units which are selectively deactivatable; a gating module (120) configured to deactivate at least one deactivatable unit based on the input segment (102) and / or on at least one intermediate output segment (112, 123), an output interface (103) configured to provide an output (104) derived from the last layer of the NN.

A computer system of a vehicle configured to monitor vehicle surroundings is provided. The computer system comprises processing circuitry configured to acquire data samples from a monitoring device configured to measure a distance to an object located within a field of view of the monitoring device, determine a difference between at least two sequential data samples of the measured distance from the monitoring device to the object, and if said difference exceeds a set first threshold value, determine a number of distances within an operating range of the monitoring device to the object that are not measured by the monitoring device during a set time period. The measured distance to the object is not relied upon if the number of distances that are not measured during the set time period is below a set second threshold value.

Described herein are techniques for stacking machine learning models to better capture deterministic relations in a dataset. In some instances, a first machine learning model may not be capable of capturing all of the deterministic relations in a dataset due to the limitations of the model. Supplemental models may be trained so that the corrections generated by the supplemental models, when combined with the first machine learning model, perform better at capturing the deterministic models in the dataset. Techniques are described for training supplemental models to capture deterministic relations associated with ordinal data and nominal data and continuous data.

It is desired to provide a technique for accurately estimating model parameters by data assimilation.SOLUTION: And a data assimilation unit configured to obtain a data assimilation result by executing sequential data assimilation on the basis of observation data obtained by observing a state of an observation target, the simulation result, and a variance of system noise in the simulation unit, in which the data assimilation unit makes a variance of system noise at an end of the sequential data assimilation smaller than a variance of system noise at a start of the sequential data assimilation.SELECTED DRAWING: Figure 1

This invention provides a lossless compression and decoding method for depth images. Lossless compression includes: dividing sequential data into multiple independent image blocks according to predetermined partitioning rules; selecting a corresponding thread model based on the characteristics of the depth image; calling a thread based on the thread model and using a reversible encoding algorithm to independently encode each image block; wherein, at the start of encoding for each image block, the encoder state is reset so that the encoding of each image block does not depend on the data of the preceding image block; generating metadata containing position index and compressed data length information and assembling it with the encoded image block for output. Lossless decoding includes: reading the metadata and decoding at least one image block based on the metadata. Through the above methods, parallel encoding and decoding are achieved; precise positioning via physical offsets and random access and on-demand region decoding are supported, improving the processing response of high frame rate depth streams under low computing power environments while ensuring lossless restoration.

System receives data stream, groups sequential data segments associated with data stream until initial sequence of data segments is formed which is larger than minimum size. System groups sequential data segments which are next after initial sequence of data segments until next sequence of data segments is formed which combined with initial sequence of data segments is larger than maximum size. System determines feature value for each data segment in next sequence of data segments. System selects value from feature values, and data segment corresponding to selected value. System divides next sequence of data segments at selected data segment into part of initial group of data segments and part of next group of data segments. System combines part of initial group of data segments with initial sequence of data segments as initial group of data segments. System routes initial group of data segments or group of corresponding fingerprints for deduplication.

The invention discloses a charging pile quality inspection data management method and system based on intelligent processing, and belongs to the technical field of detection and analysis. The system comprises a data sensing module, a correlation analysis module, a quality inspection debugging module and a data storage module. The data sensing module is used for collecting detection data of a to-be-detected charging pile and quality inspection logs of charging piles of the same model. The association analysis module is used for analyzing various indexes in the detection data and mining association rules among the indexes; the quality inspection debugging module generates a detection scheme according to the association rule, and dynamically adjusts the test sequence in combination with the value of the detected index in the quality inspection process; and the data storage module is used for analyzing the detection result of each index after the quality inspection is completed, providing the detection result to quality inspection personnel for determination, generating a quality inspection record and storing the quality inspection record in a quality inspection log. According to the method, a self-adaptive detection optimization mechanism is constructed by fusing a design principle, historical data and a dynamic association rule, and the core problems of data island, decision stiffness, resource waste and the like in the prior art are solved.

A streaming engine employed in a digital signal processor specified a fixed data stream. Once started the data stream is read only and cannot be written. Once fetched, the data stream is stored in a first-in-first-out buffer for presentation to functional units in the fixed order. Data use by the functional unit is controlled using the input operand fields of the corresponding instruction. A read only operand coding supplies the data an input of the functional unit. A read / advance operand coding supplies the data and also advances the stream to the next sequential data elements. The read only operand coding permits reuse of data without requiring a register of the register file for temporary storage.

A data storage device includes a memory device and a controller coupled to the memory device. The controller is configured to interact with a host device using a Universal Flash Storage (UFS) interface protocol, provide a hint to the host device, switch between a first mode and a second mode, retrieve data from the memory device, and transfer the data to the host device. The hint includes an indication of what order data is to be received from the data storage device. After the hint is provided, the order of the data will be a different order than a requested order.

The present invention relates to a sequential recommendation method and system with multi-behavior and multi-comparison views. The method includes the following steps: Step A: Collect multi-behavior data generated by a user's interaction with items and construct a multi-behavior and multi-view training set; Step B: Use the training set to train a deep learning network model for sequential recommendation. The deep learning network model utilizes the globality of graph information and the individuality of sequential information to complement and enhance each other. At the same time, data augmentation is performed on the graph data for contrastive learning to learn more robust representations, and data augmentation is performed on the sequential data for contrastive learning to solve the problem of sequential sparsity and further improve the representation capabilities of the graph and the sequence itself; Step C: Input the user behavior data into the deep learning network model in sequence and output the corresponding recommendation results for the current user. This method and system are beneficial to improving the satisfaction of user recommendation results.

Methods, systems, and devices for physical prefetch are described. A memory system may prefetch data from physical addresses that are sequential to a physical address that has recently been read by the memory system. For example, the memory system may determine that a rate of access for a virtual block exceeds a threshold, and the memory system may prefetch the physically sequential data in response to the rate of access exceeding the threshold. In some examples, the memory system may receive a command to read a logical block address corresponding to a first physical address. The memory system may determine that the rate of access to the virtual block exceeds the threshold. The memory system may identify one or more second physical addresses that are sequential to the first physical address, and the memory system may transfer data from the one or more second physical addresses to a buffer.

This invention discloses a method and system for processing terminal security information in wireless networks, relating to the field of terminal security information technology. The method includes collecting a physical layer feature set and an internal state feature set of the terminal; preprocessing the features using a lightweight edge computing module; arranging the physical layer feature set in chronological order to construct sequential data; recursively calculating the mean and variance of the features to generate a real-time feature baseline range; combining the terminal's internal state feature set with a weighted correction of the real-time feature baseline range to obtain a dynamic baseline range; triggering an alarm based on the dynamic baseline range; triggering re-authentication; and after three consecutive alarm triggers, feeding back to the LSTM model for online updating. This method overcomes the limitations of traditional single-feature authentication, avoids the misjudgment of legitimate signals as abnormal due to multipath effects, reduces the false positive rate through dynamic threshold adjustment, and reduces redundant operations by 30%.

Systems, methods, and circuits utilize one or multiple data-stream watchdog codes for verifying a temporal state of data from a sensor system. A data-stream watchdog system can includes a sensor system configured to detect physical phenomena and produce corresponding output signals; a memory structure configured to store the output signals as sequential data on a repeating cycle, a watchdog code generator configured to insert a watchdog code into the stored sequential data and update the watchdog code periodically, and a data transmitter configured to receive the sequential data with included watchdog code from the memory structure and transmit the sequential data with the included watchdog code over a physical data channel each cycle of the repeating cycle. An application system receives the data and a watchdog code checker checks whether the watchdog code in the sequential data is correct and produces an error indication when the watchdog code is incorrect.