67 results about "Absolute difference" patented technology

The invention provides a bedding structure rock finite element model reconstruction method based on a rock core digital image, and belongs to the technical field of digital rock cores, and the method comprises the following steps: S1, obtaining a gray level image of a rock sample; s2, calculating a transverse mean value of the analysis area to obtain a longitudinal gray level distribution curve; s3, applying moving average filtering and smooth filtering to obtain a smooth gray curve; s4, first-order difference is carried out on the smooth gray level curve, the position with the absolute difference value larger than a threshold value is taken as an initial stripe boundary candidate, and starting and stopping pixels, the width and the average gray level of each stripe are determined; s5, mapping each clustering label interval into a physical interval based on FEM grid partition; and S6, constructing a sample finite element model, and generating a reconstructed finite element model with real partition information. According to the method, the macrostructure features of the rock can be efficiently recognized and extracted by directly utilizing the pictures and combining gray profile analysis, and efficient and low-cost reconstruction of the finite element model of the bedding structure rock is achieved.

The invention relates to the technical field of dam safety monitoring, and discloses a multi-source remote sensing data deep learning dam slope monitoring method, which comprises the following steps: resampling each mode to a common ground grid, and calculating robust statistics and a time stability agent on grid and block scales; determining a reference image according to the block-level robust score, and adaptively setting a local displacement search range with a high-intensity centroid difference; evaluating the discrete candidate displacement in a grid neighborhood by using a median absolute difference to obtain a local displacement field and a residual error; three types of subitems are constructed based on robust noise, time stability and registration residual errors, and pixel-level reliability weights are adaptively synthesized through logarithmic variance proportions among modes; analyzing and solving local linear mapping in a neighborhood by using a weight-weighted observation matrix, and calculating a weighted residual error according to the local linear mapping; a binary and probability anomaly graph is generated with a robust threshold.

The present invention discloses a defect detection method, device, computing device and storage medium for an annular workpiece. The method is executed in a computing device and includes: obtaining an image of the annular workpiece to be detected; extracting an annular region of interest from the image to be detected and expanding it to obtain a rectangular expansion image to be detected; determining the pixel grayscale probability of the rectangular expansion image to be detected based on a grayscale probability distribution matrix to construct a probability map and determine the defect area in the probability map; performing grayscale repair on the defect area in the probability map to obtain a repaired map; constructing an adaptive defect-free template based on the repaired map; calculating the absolute difference between the adaptive defect-free template and the rectangular expansion image to be detected to obtain a defect residual map, so as to locate the defect position of the annular workpiece based on the defect residual map. According to the technical solution of the present invention, the detection effect of large-scale defects of annular workpieces can be improved, high-precision detection can be achieved, and the algorithm is optimized to improve the efficiency of defect detection.

This invention relates to the field of radiation measurement technology, specifically to an authentication method for encrypted radiation measurement. It addresses the problem that existing technologies lack mechanisms to effectively shield and minimize the disclosure of sensitive information while maintaining identification efficiency, making them unsuitable for application scenarios where privacy protection demands are increasingly prominent. The method includes obtaining irradiation image data of a reference item and a test item, scaling their sizes to N×N, and denoting them as D₁ and D₂ respectively; introducing a key matrix λ; obtaining encryption matrices α₁ and α₂ through matrix multiplication of λ with D₁ and D₂ respectively; obtaining an absolute difference matrix β; obtaining a hash matrix σ; when β(i,j)>μ(i,j), setting σ(i,j) to 1, indicating that at this position, the difference between the reference item and the test item has exceeded the maximum difference, and the difference index increases by 1; when β(i,j)≤μ(i,j), setting σ(i,j) to 0, indicating that there is no difference between the two items at this position; scrambling and reducing the dimension of the hash matrix σ to obtain a hash sequence denoted as F, and using the elements of the hash sequence and H as the final difference index to accurately determine the identity or legitimacy of the test item.

Anomaly detection for streaming data is provided. A spiking neural network receives inputs of streaming data, wherein each input is contained within a number of neighborhoods and converts the inputs into phase-coded spikes. A median value of each input is calculated for each size neighborhood containing the input, and an absolute difference of each input from its median value is calculated for each size neighborhood. From the absolute differences, a median absolute difference (MAD) value of each input is calculated for each size neighborhood. It is determined whether the MAD value for any size neighborhood exceeds a respective threshold. If the MAD value exceeds its threshold, an anomaly indication is output for the input. If none of the MAD values for the neighborhoods exceeds its threshold, a normal indication is output for the input.

This application discloses a method for dynamically adjusting the analog signal filtering coefficient and a temperature measuring device, relating to the field of industrial control technology. The method updates a continuously changing counter by comparing the sampled value with the current output value in real time, and dynamically adjusts the filtering coefficient based on the comparison result of the absolute difference between the two values ​​and preset first and second thresholds. After limiting, the output is updated based on whether the continuous trend of the counter exceeds a set value. This method fundamentally solves the inherent contradiction of traditional fixed-window filtering in simultaneously suppressing spike interference and rapidly responding to changes in real-world operating conditions. The processor of the temperature measuring device is configured to execute the above method, and combined with the three-wire connection method and matching resistor design of the hardware, it synchronously eliminates measurement errors introduced by long wires, achieving an adaptive balance between measurement anti-interference, accuracy, and system response speed. It is particularly suitable for temperature measurement scenarios with high reliability and high dynamic requirements.

The present application relates to remote meter reading technology field, specifically to a kind of intelligent monitoring method and system of internet of things ultrasonic water meter, comprising the following steps: obtaining three cycle pressure difference algorithm absolute difference accumulation determination fluctuation range, then sampling flow rate sequence algorithm standard deviation determines stability, according to state extraction data algorithm first-order difference parity ratio determines credibility, low credibility then weighted replacement of preceding and subsequent values, with replacement value judges fluctuation restores initial frequency.In the present application, pipeline state change is identified by cycle pressure difference value accumulation, data fluctuation identification capability is enhanced, sampling frequency is dynamically adjusted by introducing flow velocity fluctuation degree, response speed is improved, data credibility is judged by first-order difference ratio, low credibility period is weighted to improve data stability and continuity, sampling recovery state is judged by comparing preceding and subsequent periods, sampling frequency is ensured to be timely homing, adaptive closed-loop dynamic mechanism is constructed, data accuracy and continuity under complex working conditions are enhanced.

The invention relates to the technical field of logistics management, in particular to an AI-based logistics demand prediction method and system, and the method comprises the following steps: recognizing nodes based on a period difference value and high-frequency fluctuation, constructing a graph link, and predicting a logistics demand trend. According to the method, accurate screening and positioning of key time nodes are realized by introducing inter-node difference accumulated absolute difference and a local high-frequency fluctuation accumulation mechanism and fusing periodic change and high-frequency feature recognition, a path structure is constructed through inter-node time sequence intervals, and a map link relationship is extracted; a logistics demand potential fluctuation rule is extracted from a sequence and a connection rule of a map path, and then is converted into standard structure data through AI for prediction output, so that the problem that a traditional model is insensitive to non-linear fluctuation identification can be effectively solved, the response capability to multiple change trends is enhanced, and the prediction accuracy is improved. The adaptability and effectiveness of the prediction result in a dynamic scene are improved, and the identification precision and prediction granularity of the multi-stage multi-feature logistics demand are significantly improved.

The application relates to the technical field of data security and discloses an industrial computer data security protection method based on edge computing, which comprises the following steps: synchronously collecting the arrival time, byte size, running voltage and absolute temperature of a data packet; smoothing a temperature step by using a real-time voltage modulation coefficient to obtain an adaptive temperature change rate; stripping a linear drift rate of an observation window, combining a temperature proportion to extract an intrinsic heat power deviation; mapping an expected calculation load by using a voltage drop depth; extracting a communication energy footprint based on a data packet time sequence; calculating an absolute difference value of a normalized heat power and the expected calculation load, and amplifying the absolute difference value through the communication footprint to obtain a cross-domain security abnormality deviation; and finally generating a dynamic threshold according to a four-order moment feature of a deviation sequence deviation to perform abnormality determination. The application eliminates environmental thermal drift and quantization noise interference, decouples legal control task fluctuation, and realizes efficient and low-delay data security protection.

This application relates to a multi-representation expert selection method and system based on instance feature reliability. The method constructs V-I trajectory image feature representations and frequency domain statistical feature vectors for voltage and current data in the steady-state operating range, respectively. Image expert models and frequency domain expert models with fixed input parameters are used to obtain prediction outputs. Based on the prediction probability distribution of each expert, the maximum prediction probability, the second-largest prediction probability, and the difference between the first two probabilities are extracted to construct local reliability description vectors for image experts and frequency domain experts. The reliability auxiliary feature vector is obtained by calculating the element-wise absolute difference. This vector is concatenated with the frequency domain statistical feature vector and input into a gating decision module to generate expert selection weights. The load identification result is obtained by weighted fusion of the prediction outputs of each expert. This application can adaptively adjust the expert output weights according to the differences in the discriminative reliability of each representation under different instances, improving the accuracy and stability of load identification.

The invention provides a semi-global stereo matching method based on dynamic cross aggregation and multidirectional scanning line optimization, and relates to the technical field of computer vision and three-dimensional reconstruction. According to the method, the initial matching cost is constructed by fusing the pixel absolute difference sum matching cost and the sorting transformation matching cost, the dynamic cross cost aggregation region is constructed adaptively based on the color difference and the spatial distance, and on this basis, semi-global cost optimization is executed by adopting four-way scanning line iteration to generate the disparity map. And further combining left and right consistency check, region voting update, shielding filling, edge constrained neural network residual correction and sub-pixel fitting to output an optimized disparity map, thereby improving the disparity matching stability and overall consistency of a weak texture region and a depth discontinuous region.

An original adaptive variable rate shading method includes generating a first absolute difference in depth in an x direction, generating a second absolute difference in depth in a y direction, adding the first absolute difference and the second absolute difference to generate a third absolute difference, and setting a variable shading rate based on at least a plurality of threshold values and the third absolute difference.

This invention proposes a wafer defect detection method based on a vision transformer model, belonging to the interdisciplinary field of semiconductor manufacturing and computer vision technology. The method acquires top views of the wafer from both a normal and defective perspective and locates the defect using the absolute difference method. Information fusion is performed on the located areas to amplify the defect features, which is more conducive to model learning. An L-Vit structure is proposed, which transforms the data after absolute difference processing into a vector sequence and combines it with the vector sequence from the defective perspective to further highlight the defect features. This invention proposes a novel multi-head self-attention mechanism, p-MHA, which incorporates a formula relating q and v. The main purpose is to strengthen the connection between the absolute difference sequence vector and the vector sequence from the defective perspective, while reducing the data volume of the multi-head attention mechanism and the model itself.

The invention discloses a block chain-based public data authorization operation management system and method, and relates to the technical field of block chain data analysis, and the method comprises the steps: carrying out the statistics of the successful call number of each block chain, calculating the successful call rate, obtaining the levels of a call department and a data department, carrying out the assignment, and calculating the absolute difference value of the levels, establishing a training cycle updating model in combination with the successful call rate, the absolute difference value and the training factor; the method comprises the following steps: monitoring a calling condition, obtaining real-time request characteristics, judging a calling record state, calculating a difference value of a to-be-audited record, comparing the difference value with a threshold value to determine whether to authorize, calculating a next training period of each block chain, calculating a next training period of a competent department according to the next training period of each block chain, and organizing training. The risk of data leakage and abuse is reduced, the operation efficiency and accuracy are improved, and the occurrence rate of human errors is reduced.

The invention discloses an attack behavior tracing method and system in a honey court environment, and the method comprises the steps: enabling a real-time attack trapping path to be matched with a preset navigation path when an attack behavior is detected in the honey court environment, and screening out a candidate node set which meets a multi-dimensional judgment condition; selecting an optimal traceability node by calculating an absolute difference value between the distance between each node and an attack source and a preset threshold value; detecting whether a hidden jump node based on information, protocol and topological feature judgment exists on a link between the node and an attack source or not; and if yes, counting an attack position variable quantity in a monitoring time window, and adaptively deciding whether to plan a bidirectional prediction path for comparison or to switch to a reverse tracking direction through comparison with a dynamic threshold value. According to the method, the problems of inaccurate path screening, inaccurate node positioning, insufficient hidden identification and direction solidification are solved, and the accuracy, continuity and adaptive capacity of tracing are improved.

The invention discloses a table look-up type deformation matching and electromagnetic mapping digital twinning system and a table look-up type deformation matching and electromagnetic mapping digital twinning method. The system pre-constructs a multi-physical field simulation database with speed as a main key, stores a deformation three-dimensional model, pressure field data, radar cross section (RCS) data and feature point displacement, and establishes a reduced scale model deformation-speed association list. In the manual mode, the speed is directly input to call corresponding multi-physical field data; in an automatic mode, the displacement of the feature points of the reduced scale entity model is detected in real time through a displacement detection module, the current deformation quantity is calculated, a speed major key is reversely matched by adopting a minimum absolute difference method based on the association list, and then corresponding multi-physical field data is called; and synchronously displaying the deformation three-dimensional model, the pressure field pseudo-color distribution and the RCS map on a visual rendering platform, and realizing digital mapping from entity deformation to a multi-physical field state.

An electronic device or method for adjusting a gain on a voice operated control system can include one or more processors and a memory having computer instructions. The instructions, when executed by the one or more processors causes the one or more processors to perform the operations of receiving a first microphone signal, receiving a second microphone signal, updating a slow time weighted ratio of the filtered first and second signals, and updating a fast time weighted ratio of the filtered first and second signals. The one or more processors can further perform the operations of calculating an absolute difference between the fast time weighted ratio and the slow time weighted ratio, comparing the absolute difference with a threshold, and increasing the gain when the absolute difference is greater than the threshold. Other embodiments are disclosed.

The application discloses a kind of attack behavior tracing method and system under honey yard environment, method includes: when detecting attack behavior in honey yard environment, real-time attack trapping path is matched with preset navigation path, and candidate node set satisfying multidimensional determination condition is screened out;By calculating the absolute difference value of the distance of each node and attack source with preset threshold value, the optimal tracing node is selected;Detect whether there is hidden jump node based on intelligence, protocol and topological feature determination on the link between the node and attack source;If there is, then the attack position variation in the monitoring time window is counted, and by comparing with dynamic threshold, it is adaptively decided whether to plan a two-way prediction path for comparison or switch to the reverse tracking direction.The application solves the problems of inaccurate path screening, inaccurate node positioning, insufficient hidden identification and direction fixation, and improves the accuracy, continuity and adaptive ability of tracing.

The invention discloses a spectrum quality control system and method based on a dynamic repeatability limit rule base and a storage medium, and relates to the technical field of analytical chemistry and spectrum analysis quality control. The system comprises a data import module, a dynamic repeatability limit rule base, an intelligent judgment module, a result export module and a data storage module. The system manages complex repeatability limit rules related to the content through a structured rule base, and dynamic calculation of the rules is achieved through a built-in formula analysis engine. The intelligent judgment module takes an absolute difference value of two times of measurement as a core judgment basis, performs statistical expansion processing on multiple times of measurement conditions, completes comparison and judgment in batches through a full-automatic process, and finally outputs a visual report and archives full-process data. According to the invention, the technical problems of complex repeatability limit rule, low manual processing efficiency and error proneness in high-throughput spectrum analysis are solved, and efficient, accurate and traceable automatic quality control is realized.

The application provides a bedding structure rock finite element model reconstruction method based on a core digital image, and belongs to the technical field of digital cores and comprises the following steps: S1: acquiring a gray image of a rock sample; S2: calculating and analyzing the horizontal mean value of a region to obtain a longitudinal gray distribution curve; S3: applying a sliding average filter and a smoothing filter to obtain a smoothed gray curve; S4: performing first-order differentiation on the smoothed gray curve, taking positions with an absolute difference value greater than a threshold value as initial strip boundary candidates, and determining the start and end pixels, the width and the average gray of each strip; S5: mapping each clustering label interval to a physical interval based on FEM grid partition; and S6: constructing a sample finite element model to generate a reconstructed finite element model with real partition information. The application can directly utilize a photo in combination with gray profile analysis, efficiently identify and extract macroscopic structure characteristics of rock, and realize efficient and low-cost reconstruction of a bedding structure rock finite element model.

The application relates to the technical field of image intelligent analysis, and specifically discloses a feature extraction method and system based on deep learning and imageomics, which comprises the following steps: through calculating the overlap rate of deep learning and imageomics ROI boundaries and the absolute difference of Z-score standardization of GLCM-entropy, double determination and accurate identification of ROI boundary double inconsistency are realized; based on a spatial attention mechanism, a pixel weight distribution model is constructed in combination with texture abnormality, abnormal weight elimination, smoothing normalization processing and adaptive filtering of pseudo boundaries are carried out, a unified coordinate system with the imageomics boundary center as the origin is re-constructed, image standardization is realized by minimizing and optimizing affine transformation parameters through the Hausdorff distance, finally, a three-order Bezier curve is used to fit the dislocation area and iterative smoothing correction is carried out, high-precision unified ROI boundaries are established, image correction and efficient fusion of heterogeneous features are realized, and the effectiveness of coupled feature representation is improved.

The application belongs to the field of new generation information technology, and particularly relates to a low-angle fast target radar multipath effect compensation method based on time sequence characteristics. The application obtains a three-dimensional measurement residual time sequence, performs continuous wavelet transform on a pitch residual sequence in the sequence, reconstructs a pitch multipath error term, eliminates the pitch multipath error term in the pitch residual sequence, and obtains a compensated pitch residual sequence. Then, Gaussian test is performed on the compensated pitch residual sequence, a statistical quantity is calculated, and judgment is made according to the absolute difference between the adjacent two statistical quantities. If the iteration does not converge, the next iteration is started. If the iteration converges, the current motion state estimation value and the compensated pitch measurement sequence are output, and the compensation of the low-angle fast target radar multipath effect is realized. Through the application, the effective compensation of the low-angle fast target multipath error term can be realized. Neither front-end hardware modification is involved, nor good adaptability to the environment is required.

A method for deblocking at least one boundary of a coding unit, wherein the boundary forms a first side and a second side of the coding unit, wherein a video picture comprises the coding unit, and wherein the first and second sides use a same reference picture. The method includes obtaining a first motion vector component in the first side; obtaining a second motion vector component in the second side; determining whether an absolute difference between the first motion vector component and the second motion vector component is equal to or larger than a predetermined threshold that is less than 1 sample; and as a result of determining that the absolute difference between the first motion vector component and the second motion vector component is equal to or larger than the predetermined threshold, applying deblocking to the vertical or horizontal boundary between the first side and the second side.

The present application relates to the field of distributed computing and provides a method, device, and computer-readable storage medium for processing out-of-order data, so that the set delay time responds objectively, reasonably, and promptly to the fluctuations of data when processing out-of-order data. The method comprises: calculating the absolute difference between the timestamps of each of n out-of-order data and the previous non-out-of-order data in the last event processing cycle to obtain n-1 timestamp absolute differences; determining the delay time T according to the n-1 timestamp absolute differences; d ; In the current event processing cycle, according to the timestamp and delay time T of the data received in the current time window d , determine the current latest water level; according to the current latest water level, the right boundary of the current time window and the delay time T d , determine whether to trigger the current time window to start processing the data received in the current time window. The technical solution of this application makes the calculation of the latest watermark more objective and also makes the response to data disorder more timely.

The present application relates to the technical field of distributed account book and data processing, and discloses a supply chain risk real-time monitoring system and method, which comprises a consensus account book data module, a parameter configuration storage module, a digital asset locking module and a contract operation processing module; the absolute difference value between the block timestamp of a target account book node and the hierarchical supply data reporting timestamp is read by the contract operation processing module, and then divided by the calibration cycle length in the parameter configuration storage module to obtain the account book time sequence bias rate; the account book time sequence bias rate is compared with the discrete control threshold value, and according to the comparison, the clearing channel is released, the credit allocation weight parameter is adjusted, or the digital asset locking module is triggered to deduct the performance guarantee margin asset, the delay behavior of the target account book node is translated into an internal time sequence deflection parameter, the dependence on off-chain data is broken, a clearing closed loop is constructed, and risk adaptive feedforward hedging is realized.

Methods and apparatuses of determining an alignment level between motion compensated reference patches for reducing motion vector refinement steps are provided. One method implemented by a decoder includes, obtaining motion compensated interpolated samples based on sub-pixel accurate merge motion vectors from a bilinear motion compensated interpolation, computing a sum of absolute differences (SAD) between two motion compensated reference patches using a subset of the motion compensated interpolated samples, and determining whether the SAD is less than a coding unit (CU) size-dependent threshold value. The method further includes when the SAD is less than the CU size-dependent threshold value, skipping remaining decoder-side motion vector refinement (DMVR) process steps and performing final motion compensation. When the SAD is not less than the CU size-dependent threshold value, performing the remaining DMVR process steps and performing the final motion compensation.

The invention discloses a method for reducing resource overhead in a data resampling process. The method comprises the following steps: calculating a ratio of an input sampling rate to an output sampling rate of a rate matching module and an absolute difference value between 1 and the ratio; when the input sampling rate is smaller than the output sampling rate, data are read from the FIFO buffer according to a read enable signal and a read pointer, and after each time of reading, the read pointer is moved forwards by one bit, and absolute difference values are accumulated; when the accumulation result of the absolute difference is greater than or equal to 1, keeping the read pointer unchanged and reading again for interpolation; when the input sampling rate is greater than or equal to the output sampling rate, writing data into the FIFO buffer according to the write enable signal and the write pointer, and after each time of writing, forwards moving one bit of the write pointer and accumulating absolute difference values; and when the accumulation result of the absolute difference is greater than or equal to 1, pulling down the write enable signal and discarding the current write-in data to perform value extraction. According to the invention, under the condition of not depending on a high-frequency clock and a large-depth FIFO, the resource overhead in the data resampling process is reduced.

The method for performing cross-hand palmprint recognition uses a semi-Siamese neural network to compare a first hand (e.g., a right hand) anchor embedding, which is obtained through feature aggregation of first hand images, to a second hand (e.g., a left hand) query embedding. The semi-Siamese neural network's branches share a feature-extraction backbone and employ distinct final-layer weights to allow hand-specific calibration while maintaining a common embedding space. A hybrid similarity is computed, which includes both an element-wise absolute-difference (EAD) vector and a Euclidean-distance scalar. These are concatenated and input to a classifier to produce a match score or decision regarding authentication.

The application discloses a public data authorization operation management system and method based on a block chain, relates to the technical field of block chain data analysis, and calculates the success calling quantity of each block chain and the success calling rate, obtains the levels of calling departments and data departments and performs value assignment, calculates the absolute difference value of the levels, combines the success calling rate, the absolute difference value and a training factor to establish a training cycle updating model; monitors calling conditions, obtains real-time request features, judges calling record states, calculates the difference value of records to be audited, compares the difference value with a threshold value to determine whether authorization is performed, calculates the next training cycle of each block chain, calculates the next training cycle of a supervisory department according to the next training cycle of each block chain and organizes training, so that the data security is improved, the risk of data leakage and abuse is reduced, the operation efficiency and accuracy are improved, and the occurrence rate of human errors is reduced.