43 results about "Data truncation" patented technology

The invention discloses a structural damping ratio identification method and device considering frequency spectrum leakage error compensation. The method comprises the following steps: firstly, obtaining a cut-off free attenuation signal under impact vibration, and carrying out Fourier transform on an autocorrelation function and a cross-correlation function of the signal to obtain a response power spectral density matrix influenced by a spectrum leakage error; singular value decomposition is carried out on the response power spectrum density function through a frequency domain decomposition method to obtain a damping ratio and an inherent frequency, a spectrum leakage error is calculated through the calculated inherent frequency and the damping ratio, and the error obtained through calculation is substituted into an output power spectrum. And the inherent frequency and the damping ratio are identified by using the power spectrum after the frequency spectrum leakage error is compensated, and the above process is iterated for multiple times, so that the frequency spectrum leakage error in damping ratio identification can be compensated. When the free attenuation data is used for identifying the damping ratio, spectrum leakage errors caused by data truncation can be made up, the signal-to-noise ratio of the data can be improved, the influence of noise can be reduced, and the identification precision of the damping ratio is improved.

The invention discloses an emotion recognition method based on an attention mechanism and a capsule network. The emotion recognition method specifically comprises the following steps: inputting multi-channel original EEG signals; performing data truncation, data filling and data standardization on the original EEG signal, unifying the format, and generating a preprocessed EEG signal; extracting space-time joint features from the preprocessed EEG signals by using a three-dimensional convolutional neural network; the construction of the graph structure comprises modeling a complex relationship between channels; introducing a graph attention mechanism to strengthen information expression of important channels and relationships; and high-dimensional vector modeling and final classification of the features are carried out through a capsule network, and capture of deeper space structure information is completed.

The mixed-base FFT hardware accelerator comprises an input logic unit, an operation logic unit, a data conversion unit and an output logic unit which are arranged on an FPGA and connected in sequence, an operation unit is arranged in the operation logic unit, the operation unit is connected with the data conversion unit, the systolic array is arranged in the operation unit, and the systolic array is connected with the data conversion unit. The pulse sequence adopts mixed base to accelerate calculation of FFT (Fast Fourier Transform); the data conversion unit comprises a data truncation module and a data splicing unit which are connected in sequence, the operation unit is connected with the data truncation module, and the data splicing unit is connected with the output logic unit; the input logic unit, the operation unit, the data truncation module, the data splicing unit and the output logic unit are all connected with the controller, and the input logic unit, the data splicing unit, the data splicing unit and the output logic unit are all connected with the DDR memory through a data bus. The method can adapt to requirements of different performances, and fast calculates FFT of different points.

The invention discloses an urban tunnel limit invasion prevention intelligent early warning method based on distributed acoustic sensing, and relates to the field of underground monitoring and rail safety early warning, and the method specifically comprises the steps: inputting a data record of a distributed optical fiber; the records are subjected to standard size windowing, spectral amplitude solving, range intercepting, data truncation and normalization; training a convolutional auto-encoder to reconstruct data, and performing encoding dimension reduction on the data by using an encoder part; training a random forest classifier by using the feature vectors subjected to real-time data preprocessing and dimension reduction; continuous data is preprocessed, and after dimension reduction of the data by a convolution encoder, detection and classification are carried out by using a random forest; and performing post-processing based on signal space-time continuity screening on a detection result to obtain an accurate detection and classification result. According to the invention, the construction signals in the distributed acoustic sensing data are monitored in real time by using the convolution auto-encoder and the random forest algorithm, detection and accurate classification are carried out with high temporal-spatial resolution, and intelligent early warning of track construction based on distributed optical fibers is realized.

The present invention discloses a smart grid data aggregation method based on w-event-level local differential privacy. The smart meter processes historical data locally to find the optimal endpoint value for data truncation. Secondly, at each moment, the user groups the data stream by deviation distance based on the correlation of the grid data. When the deviation distance is small, the data is divided into a group and temporarily stored at the smart meter end. The value of the privacy budget is set, and the meter is disturbed locally. The data in the same group only reports one disturbed value. Finally, after receiving the report value of a regional smart meter, the gateway performs aggregation calculation on the data at that moment and sends the aggregated value to the control center. Compared with traditional protection schemes, the present invention does not rely on trusted gateways, and reduces computing and storage overhead while protecting the user's electricity usage pattern.

The embodiments of this specification provide a method and device for fusion processing of truncation and ring-raising in secure multi-party computing. The method includes: the first party uses the first ring arithmetic slice of its own party to perform a first multi-party joint calculation with the second party to obtain the Boolean slice of its own party with an overflow indication bit; the overflow indication bit is used to indicate whether the sum of the two first ring arithmetic slices of the target private data overflows the first ring; the Boolean slice of its own party with the overflow indication bit is used to perform a second multi-party joint calculation with the second party to obtain the overflow bit arithmetic slice of the overflow indication bit secretly shared on the second ring; the number of bits of the second ring is determined according to the number of bits of the first ring, the number of bits of the target ring of the ring-raising, and the number of bits of the truncation; based on the first ring arithmetic slice of its own party and the overflow bit arithmetic slice, a local operation is performed to obtain the target ring arithmetic slice of the target private data that is secretly shared on the target ring after truncation. It can significantly reduce the number of communication rounds and communication volume.

The invention discloses an offset scanning CBCT image reconstruction method. The method comprises the following steps: virtual detector conversion of projection data; carrying out geometric weighting on the data; carrying out data continuation and filtering; parker weighting is carried out on the data; and reconstructing bias back projection. According to the invention, aiming at the scanning mode of radiation source and detector bias, the FDK algorithm cannot be directly used for reconstruction, the virtual detector is introduced to convert the scanning mode into the scanning mode of detector bias, the data is subjected to continuation processing to solve the problem of data truncation, and the filtered data is subjected to Parker weighting aiming at the repeated scanning area to solve the problem of bias artifacts. And finally, carrying out three-dimensional reconstruction by adopting an FDK reconstruction method. The reconstruction method is high in speed, and the obtained cross-sectional image is high in definition.

The application relates to the technical field of data processing, and discloses a network public opinion hotspot evaluation system based on sentiment analysis, which comprises the following modules: a data analysis module, which is used for receiving an interactive flow extraction identifier and text and calculating a basic sentiment polarity value; a window control module, which outputs a current time window span based on a topological entropy gradient of a previous period; a topological layering module, which constructs a local directed graph to calculate a global topological entropy, and takes a shortest path hop count as a topological depth; a polarization rate calculation module, which allocates and calculates a variance of the polarity value according to the topological depth and outputs a sentiment polarization rate; and a comprehensive evaluation module, which combines a word frequency, the sentiment polarization rate and an activation function to output a comprehensive hotness evaluation value. The application adaptively adjusts data truncation according to network public opinion fluctuation, prevents feature dilution through spatial layering sentiment analysis, and automatically peels off abnormal concurrent flow based on a topological base, so that the hotspot evaluation is real and objective.

The present application relates to the field of spaceflight TT&C technology, and specifically discloses a method and system for implementing an extraterrestrial object blocking TT&C plan under deep space large time delay conditions. The present application determines extraterrestrial object blocking time windows and ground available tracking stations by using precise orbit data of a probe, calculates station prediction containing data truncation and one-way light time of uplink and downlink signal transmission, sets uplink tracking events in advance and downlink tracking events in arrears according to the one-way light time, merges adjacent TT&C arcs with a time interval less than a preset threshold into single-station continuous tracking arcs to set task preparation events, and decouples uplink and downlink under deep space TT&C mode, controls transmission of uplink carriers and remote control instructions according to the one-way light time, thereby solving the technical problems of difficulty in space-earth cooperation and poor timeliness of emergency retransmission during extraterrestrial object blocking under deep space large time delay conditions, and improving the reliability and timeliness of implementation of deep space exploration mission TT&C plans.

Implementations of a method of mitigating lag for an image sensor device may include reading a row of data from an Nth frame of image data from an image sensor device; correcting the row of data using truncated row data from an N-1th frame stored in a memory operatively coupled with the image sensor device to form a lag corrected row of data; outputting the lag corrected row of data; truncating the row of data to form truncated row data from the Nth frame; and storing the truncated row data from the Nth frame in the memory.

The invention provides a log cleaning and archiving method and system, a readable storage medium and device.The method responds to a trigger signal, whether redundancy exists in log data in a log data list or not is judged, and if yes, redundant data is filtered; dividing the filtered log data according to the data size, obtaining the log data smaller than a threshold value in batches, and obtaining the log data larger than or equal to the threshold value one by one; judging whether the obtained log data belongs to a big data field exceeding a set value or not, and if yes, performing truncation or keyword replacement on the big data field, and storing the processed log data into an archiving table or an archiving file; and deleting the corresponding data in the current log data list in batches. By optimizing a log storage mechanism, a data truncation processing strategy, a reduction and retention strategy, a storage strategy and the like, the data security is improved, the processing efficiency is improved, and high flexibility is achieved.

The invention discloses a differential dynamic microscopic image processing method based on a root-mean-square error. The differential dynamic microscopic image processing method comprises the following steps: a sequence image acquisition step: acquiring a time sequence microscopic image; a sequence image processing step: obtaining a difference image data set; performing one-dimensional Fourier transform in two directions on the differential image data set in sequence to obtain two-dimensional transform result data, and calculating to obtain Fourier amplitude spectrum image data; an obtained data processing step: carrying out dimension reduction processing on the Fourier amplitude spectrum image data; introducing structure function fitting, calculating a root-mean-square error between a fitting curve of the data after dimension reduction processing and the Fourier amplitude spectrum image data, and selecting a data truncation point according to the root-mean-square error; and an intermediate scattering function is introduced, and dynamic information of Brown particles is obtained based on the relationship between the data after selection of the truncation point and the diffusion coefficient. According to the method, the data truncation point is determined through the root-mean-square error, so that the processing time is remarkably shortened while the measurement precision is ensured.

The invention provides a marketing suggestion generation method, device and equipment for a target community and a storage medium, and the method comprises the steps: selecting a full-field user feature data stream of a target user group, which accords with a preset rule, from a user portrait system of the target community, reading a data compression operator combination corresponding to a marketing report type from an operator configuration library, each operator defines a specific data extraction and compression rule. And sequentially executing each data compression operator, performing multi-dimensional analysis and information extraction on the user feature data, generating a compressed feature description text fragment, and compressing the original mass data of millions of tokens to the scale of hundreds of tokens. According to a predefined template structure, all feature description text segments are combined in order to form a user portrait abstract, the user portrait abstract is embedded into a complete cue word template based on a feed-shot cue word construction method, it is ensured that LLM can generate a precise marketing strategy based on complete and refined user information, and the problems of data truncation and attention loss are effectively avoided.

The invention is suitable for the technical field of power spot market clearing, and provides a power delivery in-province power spot market clearing method, and the method comprises the steps: carrying out the calculation of the delivery superposed power of a target unit in a current transaction period based on the obtained delivery power data and technical parameter data; generating a delivery newly-added curve segment based on the delivery superposed power and the acquired market price lower limit; performing data translation processing on each section of the original declaration curve data based on the outgoing superposed power, and performing data truncation processing on the declaration curve data after data translation processing to obtain an effective curve section after data truncation processing; performing data splicing on the delivery newly-added curve segment and the effective curve segment to generate a reconstructed declaration curve of the target unit; based on the reconstructed declaration curve, clearing the current transaction period in the power spot market in the province; the accuracy of clearing calculation input data is ensured, and the reliability of clearing price signals in the provincial spot market is improved.

The present invention relates to a method for inserting and extracting additional information into content data, and provides a method and an apparatus utilizing the same that are not only robust against various and multiple attacks such as data compression, data truncation, resizing, insertion of other data, and letterboxing, but also fast because the additional information is inserted into the spatial area formed by the boundary of a representative object, and capable of inserting and extracting a large amount of data into the boundary of a representative object using an additional information extraction method rather than a watermark detection method. Another objective of the present invention is to provide a method and a device utilizing the same, which enables the rapid and accurate extraction of additional information even for text data that has been edited or modified through various devices, by configuring specific sets as insertion objects and displaying additional information through base conversion in the case of text that is printed as an analog result or distributed by outputting a printed result.

This invention relates to the field of electricity meter monitoring, and more particularly to an online monitoring system and method for smart electricity meters. The system includes: periodically determining the link transmission coefficient of each electricity monitoring set based on the link throughput fluctuation index and the link balance index; determining whether each electricity monitoring set meets the transmission optimization conditions based on the link transmission coefficient; under the transmission optimization conditions, determining the data mining strategy to be executed for each target monitoring device based on the data acquisition status; when performing time series analysis, determining whether to perform data truncation compensation based on the interval between each time series truncation segment, and determining the truncation compensation method based on the electricity load coefficient of the truncation coverage area; under the condition that the set analysis is completed, determining whether to perform transmission link optimization for the target optimized set based on the proportion of key equipment and the reference equipment characteristic index, and determining the allocation transmission strategy based on the demand allocation difference value. This invention improves the data processing efficiency in the monitoring process of smart electricity meters.

The invention relates to a method for realizing spectrum analysis processing based on an FFT (Fast Fourier Transform) and frequency sweeping combination mode. The method comprises the following steps of: performing switching control through a scanning control state machine; selecting a filter according to the frequency resolution bandwidth by extracting a low-pass filter bank; windowing is carried out on the extracted IQ data in a time domain; carrying out FFT (Fast Fourier Transform) operation; calculating the FFT data truncation length, and carrying out exchange sorting processing on the FFT data after truncation; calculating the IQ data amplitude of the FFT data after truncation sorting processing; and performing detection processing on the amplitude data. The invention also relates to a frequency spectrum analysis device for realizing the combination of FFT and frequency sweeping, a processor and a readable storage medium. According to the method and device for realizing spectrum analysis processing based on the FFT and frequency sweeping combination mode, the processor and the computer readable storage medium, under the conditions of large scanning width and high frequency resolution, the speed is higher than that of traditional frequency sweeping, the continuous scanning effect is different from that of traditional frequency sweeping, and the higher the frequency resolution is, the more obvious the effect is.

The invention discloses a building system self-adaptive control method based on data truncation and modular transfer learning, and relates to the technical field of intelligent building energy consumption optimization and control, modular modeling is performed on each component of a building system, a hybrid recurrent neural network is adopted as the architecture of a building system modular model, and the intelligent building energy consumption optimization and control is realized. The hybrid recurrent neural network comprises a plurality of recurrent neural network units, and each recurrent neural network unit corresponds to each component module in the building system; after sudden change of the building system is detected, only module parameters influenced by sudden change are unfrozen and finely adjusted, and module parameters not influenced by sudden change are kept frozen; meanwhile, data before mutation is abandoned to eliminate the influence of an old domain, and only the data after mutation is reserved to perform fine adjustment on unfrozen module parameters; in addition, in the model transfer learning process, only the prediction error output by the module influenced by sudden change is used as a loss function. The invention aims to solve the problems that the domain migration efficiency is low and old domain data interferes with new domain learning.

The present invention relates to a system and method for predicting a truncated image, a method for preparing data, and a medium thereof. The method for preparing data includes a virtual simulation step for performing a virtual simulation on image data to simultaneously obtain a virtual distorted image with data truncation and a virtual precision standard image without data truncation. The method for predicting a truncated image predicts a truncated image based on a trained learning network, and the trained learning network is obtained by data training based on a data set consisting of a virtual distorted image and a virtual precision standard image obtained by the method for preparing data. A system corresponding to the method and a recording medium recording instructions that can implement the method are also provided.

The invention discloses a verifiable outsourcing computing privacy protection method based on block data truncation, which adopts a three-party architecture of participants, an aggregator and a key generation center, and takes a non-collusion cloud node as a data block distribution and computing carrier. The key generation center constructs a distributed multi-trap-door public key cryptosystem based on security parameters, splits strong private keys and distributes the strong private keys to participants and aggregators. A participant splits original data into mutually exclusive blocks through binary segmented truncation and remainder operation according to the bit number of sensitive data, and distributes the blocks to different cloud nodes. And the cloud nodes complete data block addition, multiplication and equivalence verification by adopting a carry-aware mechanism to ensure that a calculation result is consistent with original data operation. And the aggregator collects a result and performs cross check, and the collaborative participant decrypts the result and then integrates the result to obtain a final weighted aggregation result. According to the method, the problem of unbalance among verifiability, privacy and practicability of the existing outsourcing computing technology can be solved.

The invention discloses a data synchronization storage space adjustment method and device, equipment and a storage medium. The method comprises the following steps: acquiring a source end storage unit of a storage unit used by a target source end and a standby end storage unit of a storage unit used by a target standby end; whether the source end storage unit and the standby end storage unit conform to preset storage unit types or not is determined, and the preset storage unit types comprise the first storage unit type and the second storage unit type. And determining a configuration item value corresponding to a matching type according to the source end storage unit, the standby end storage unit and the matching type of the preset storage unit type. According to the configuration item value and the target synchronization data requested by the target source end, the target storage space used for storing the target synchronization data in the target backup end is adjusted, so that the reliability of the data backup disaster recovery system is guaranteed, the data truncation and messy code risks in the data migration process are reduced, the data synchronization efficiency is remarkably improved, and the user experience is improved. And the data integrity and accuracy are ensured.

This invention discloses an adaptive data truncation method and system based on variance goodness-of-fit, relating to the field of natural language processing technology. The method includes acquiring multiple documents output by a re-ranking model and their corresponding relevance scores; arranging the relevance scores in descending order to obtain an ordered relevance score sequence; starting with a cluster size of 2, progressively increasing the cluster size; and classifying the ordered relevance score sequence using a natural breakpoint classification method for each cluster size. This invention performs natural breakpoint classification and variance goodness-of-fit calculation on the ordered relevance score sequence with multiple cluster sizes, then determines the optimal cluster size based on the variance goodness-of-fit gain value of adjacent cluster sizes, and adaptively determines the truncation threshold. This allows for dynamic adjustment of the filtering boundary based on the score distribution characteristics under different query scenarios, improving the adaptability of data truncation and reducing interference from low-relevance documents.

The invention relates to the technical field of seismic data processing, and particularly discloses a self-adaptive Hanning window construction method and device, and the method comprises the steps: giving an initial adjustment parameter of an initial window function, and calculating a flag value of the initial window function; setting an adjustment interval of the mark value and a correlation condition satisfied by the initial adjustment parameter and the mark value; obtaining a final adjustment parameter through iterative operation according to the adjustment interval and the association condition; and obtaining a calculation formula of the final window function according to the final adjustment parameter. The self-adaptive Hanning window constructed by the method changes more gently in starting, ending and intermediate function value, the truncation effects of data truncation, data filtering, data fusion and the like of actual data are obviously weakened, the practical requirements are better met, and the use adaptability and convenience in the practical process are improved.

This invention provides a Maxout activation function acceleration device and method based on in-memory computing devices. The device includes: a data splitting module for splitting data into integer and fractional parts; a data truncation module for splitting the integer and fractional parts into fixed-bit lengths in binary form and transmitting them to an in-memory computing device array, while simultaneously generating corresponding shift control signals and transmitting them to an accumulator module; a comparator module for comparing the array's output, obtaining the maximum value, and outputting it; an accumulator module for shifting and accumulating the comparator module's output and outputting the accumulated result according to the shift control signal; and a weight queue storage module for storing the weights corresponding to the neural network workload and updating the weights stored in the in-memory computing device array after completing one layer of computation. This invention offers advantages such as low power consumption, high energy efficiency, adjustable precision, and low computational latency.

The present invention discloses an intelligent early warning method for urban tunnel intrusion prevention based on distributed acoustic sensing, which relates to the fields of underground monitoring and rail safety early warning. Specifically, it includes: inputting data records of distributed optical fibers; windowing the records in standard sizes, calculating spectral amplitudes, intercepting ranges, truncating data, and normalizing; training a convolutional autoencoder to reconstruct data, and using the encoder part to encode and reduce the dimension of the data; training a random forest classifier using the feature vectors after preprocessing and dimension reduction of real-time data; preprocessing continuous data, reducing the dimension of the data with a convolutional encoder, and then detecting and classifying it with a random forest; performing post-processing on the detection results based on the screening of signal spatio-temporal continuity to obtain accurate detection and classification results. The present invention uses a convolutional autoencoder and a random forest algorithm to monitor construction signals in distributed acoustic sensing data in real time, detect and accurately classify them with high spatio-temporal resolution, and achieve intelligent early warning for rail construction based on distributed optical fibers.

A magnetic field speed sensor includes one or more magnetic field sensing elements to generate a magnetic field signal indicative of a magnetic field associated with a moveable target and a controller to generate a controller output signal including a plurality of output words, each including a speed pulse and a plurality of data pulses capable of having four different amplitudes. The time between the speed pulses of consecutive output words is indicative of the speed of movement of the target. Each data pulse corresponds to a data bit having a binary value represented by a transition direction of the data pulse and the amplitude of the data pulse. Features include a programmable data pulse width or an adaptively adjustable data pulse width to avoid truncation.

Systems and methods for data truncation are disclosed. A computing system may a storage medium and a processor. The processor may be configured to identify first data of a first data structure; identify a first portion of the first data and a second portion of the first data; store the first portion of the first data as a second data structure in the storage medium, and the second portion of the first data as a third data structure in the storage medium; identify a request for the first data; based on the request, retrieve from the storage medium the second data structure including the first portion of the first data; identify a value associated with the second portion; generate second data based on the first portion and the value; and perform a task based on the second data.

Methods for asynchronously determining relational data integrity using cryptographic data structures are performed by systems and devices. Changes in current tables of relational databases are reflected in associated history tables. Cryptographic hybrid blockchain ledgers are updated with transaction records, for entry changes in current and history tables, including transaction information and hash values of corresponding entry changes. Hybrid blockchain ledgers also include root hash values of Merkle trees of transaction records in current blocks, and hash values of prior blocks. A current block receipt is asynchronously generated and provided as a single hash value from which the validity states of the tables and ledger are able to be verified. Cryptographic receipts of specific transactions reflected in table entry changes are generated and provide immutable evidence of specific transaction existence for users. Ledger-enabled tables are provided for mixed database operations with ledger-disabled tables, and temporal history table database operations are enabled.

The invention provides a data truncation method based on a large model and a related device. Specifically, the nesting depth, the node path and the node value of each data node in the to-be-truncated data can be respectively determined, the semantic importance of each node path is respectively evaluated by adopting a semantic analysis model, and the data can be truncated based on the semantic importance of the node paths and the nesting depth of the data nodes. And comprehensively judging whether data truncation operation needs to be executed on the data nodes or not. Therefore, key information and non-key information in the to-be-truncated data can be recognized in combination with the hierarchical relationship of the data nodes, the nested structure and the semantic importance of the path fields, and data truncation is performed accordingly, so that the data volume can be reduced under the condition that the key information of the data is reserved, and the data quality of the truncated data can be improved.

The invention discloses a sensor digital output data compression method, and relates to the technical field of sensor chip design, and the method comprises the steps: obtaining the original digital output data of a sensor; determining a data interception bit width N and a bit width K of a scaling factor SF based on the bit width M of the original digital output data in combination with an error requirement of a digital output dynamic range of the sensor and a data volume compression requirement; intercepting N-bit data from the original digital output data to obtain effective data segments; determining the value of a scaling factor (SF) according to the position of the effective data segment in the original digital output data; and combining the effective data segment and the scaling factor SF to obtain compressed data output by the sensor. The method maintains an extremely high dynamic range while significantly reducing the amount of data by compressing the bit width of the original digital output data.