31 results about "Computational algorithm" patented technology

The invention provides a full-slice image quality automatic control method and system based on a two-stage cascade deep neural network, and relates to the technical field of digital pathology image processing. According to the method, efficient, accurate and extensible automatic quality control is realized through the steps of a two-stage cascade detection architecture, a multi-scale feature fusion mechanism, standardized GeoJSON output and multi-dimensional quality scoring. The core of the method is that priori knowledge of pathology (firstly positioning organization and then checking quality) is converted into strict conditional probability decomposition, and the method is efficiently realized through a deep learning network. The mapping from the human cognitive process to the computational algorithm provides a new methodological enlightenment for the field of medical image analysis. Theoretical analysis and actual tests show that the method is remarkably superior to the prior art in the aspects of calculation efficiency, detection precision, cross-device generalization ability and the like, and has important clinical application value and commercial prospects.

Systems and methods for utilizing imperfect resource states in a fusion based quantum computing system. First and second entangled quantum states are received and input to a quantum computational algorithm that performs a fusion measurement on the first and second entangled quantum states. It is determined that one of the first or second entangled quantum states have an error that exceeds an error threshold. Responsive to the error exceeding the error threshold, the quantum computational algorithm is dynamically modified such that an alternative process resulting in classical measurement results is performed instead of the fusion measurement. The classical measurement results are stored in a non-transitory memory medium.

A first method includes capturing, via a camera having a first pose, a first image of a surgical environment after a change has been made to the surgical environment. The method also includes capturing, via. a. camera having a second pose that is different from the first pose, a second image of the surgical environment after the change has been made to the surgical environment. Hie method also includes using a computational algorithm to process the first image, the second image, and a model representing the surgical environment, thereby updating the model to represent the change made to the surgical environment. A second method includes training the computational algorithm to perform aspects of the first method.

Disclosed is a unified deep reinforcement learning approach for constructing rectilinear and octilinear Steiner minimum trees in the technical field of computer-aided design of integrated circuits, the method including designing an edge point sequence (EPS) based on structural characteristics of a Steiner minimum tree (SMT) to bridge a gap between deep learning model output and an SMT structure; designing a deep learning model for the EPS, and using a negative wirelength of the SMT as a reward to train the deep learning model through the deep reinforcement learning (DRL); providing a corresponding fast and accurate wirelength computation algorithm for a quality assessment of construction solutions to accelerate training of the deep learning model; and constructing diversified construction solutions of SMTs utilizing the stochastic nature of machine learning. The method of the present invention can solve rectilinear Steiner minimum tree (RSMT) and octilinear Steiner minimum tree (OSMT) problems and generate diversified routing topologies.

The invention provides a method for scheduling a test task, and relates to the technical field of computers, and the implementation scheme is as follows: obtaining a to-be-scheduled test task, a test data set and the state of computing power and bandwidth resources; based on the test task, the states of the test data set and the computing power and bandwidth resources, calculating an algorithm feature vector, a data set feature vector and a test strategy feature vector; constructing a ternary relation graph based on the algorithm feature vector, the data set feature vector and the test strategy feature vector; performing feasibility judgment and cost evaluation on each ternary relation graph; based on feasibility judgment and cost evaluation, selecting a target ternary relation graph to generate a task execution scheme; and according to the task execution scheme, binding the test task to the matched resource slot and data set for execution, so that algorithm features, data set features and test strategies can be brought into a decision together, coverage of key scenes can be completed preferentially, the resource utilization rate is improved, and queuing delay and data handling cost are reduced.

The one or more embodiments of the application provide a method, device and storage medium for evaluating running of a privacy computing algorithm, the method comprising: obtaining algorithm running information corresponding to a privacy computing algorithm to be evaluated and device configuration information of a device for running the privacy computing algorithm; inputting the algorithm running information and the device configuration information into a service model of a large language model, to predict, by the service model of the large language model, a running condition of the privacy computing algorithm on the device based on the algorithm running information and the device configuration information; the service model of the large language model being a model obtained by performing supervised fine-tuning training on a base model of the large language model based on training samples including the algorithm running information and the device configuration information and labeled with the running condition; obtaining the running condition of the privacy computing algorithm on the device predicted by the service model of the large language model, and outputting the running condition.

The application discloses a multiplexing measurement and control method and device of a three-axis micromechanical gyroscope, which comprises the following steps: firstly, a global clock is generated; in the working clock of any gyroscope, an alternating current driving signal, a feedback force signal, a preset reference phase signal and a reference displacement signal are outputted to the gyroscope by a coordinate rotation digital computer, and the gyroscope reaches a force balance state through a driving modal control loop and a detection modal control loop, and the angular velocity of the gyroscope is obtained; the three gyroscopes multiplex the same coordinate rotation digital computer in the respective working clock. The application combines time division multiplexing with a coordinate rotation digital computer algorithm, and can accurately and synchronously detect and close-loop control the three-axis micromechanical gyroscope in real time in limited field programmable logic gate array resources. The method and device can be widely applied to concurrent processing requirements of multi-axis or multi-path high-speed, high-precision and high-integration measurement and control instruments.

Disclosed is a machine learning method for curating biomarker panel, for screening of cancer(s) and system therefor, addressing the critical need for early detection through a combination of innovative biomarker research, advanced computational algorithms and integration with existing healthcare infrastructure. The biomarker panel is curated to facilitate pan-cancer screening of the patient with high sensitivity and specificity using the ML method deploying MoE (Mixture of Expert) models for accurate screening, thereby enhancing diagnostic accuracy and also empowering clinicians with actionable insights, thereby improving patient prognosis and facilitating personalized treatment strategies.

The application discloses a hardware-implemented H264 macroblock-level code rate control method, which comprises the following steps: using a five-stage pipeline to implement H264 hardware coding, specifically, IME module, FME module, IPTQ module, DBK module, EE module and WBK module are used; and the hardware-implemented macroblock-level code rate control method mainly comprises the following steps: when calculating a macroblock quantization parameter QP, the calculation is divided into two stages, coarse quantization parameter coarse_qp is calculated through texture information of the macroblock, and the value is only used for the IME module and the FME module; after the macroblock type of the macroblock is determined, the mad value of the macroblock can be obtained, and the macroblock quantization parameter mb_qp is recalculated according to the mad value of the macroblock, and the value is used for operation and processing of the IPTQ module, the DBK module and the EE module. The method avoids complex macroblock quantization parameter QP calculation in the JVT-G012 algorithm, and the algorithm is simple and easy to implement in hardware.

The embodiment of the invention provides a real-time active feedback sleep monitoring system and method based on a ring. According to the system and the method, sound information data and physiological information data acquired by a ring are acquired through a microphone array of the charging dock, localized real-time analysis of the ring is realized by adopting an edge calculation algorithm, and multi-stage vibration tactile feedback is generated based on a vibration motor arranged in the ring, so that real-time active feedback can be realized during risk pre-judgment.

The application provides a time series prediction attack method based on local disturbance. The method comprises the following steps: performing BIM gradient attack on an original sample to obtain a global attack sample; using a sliding window to intercept a local interval of the original sample, performing attack on the interval according to a differential evolution algorithm, converting individuals into time series by using a segmented function, the BIM global attack sample and the original sample to calculate fitness, selecting a better individual to input into a target model to calculate mean square error (MSE) of a prediction value, and continuously iterating the algorithm until the MSE value converges. If the MSE value converges, the local attack sample with successful attack is added to a sequence of attack samples. The application narrows the disturbance range of the attack sample, uses the differential evolution algorithm to find an optimal attack point, and combines the segmented function to segment the disturbance interval, so that the disturbance range is further narrowed, and semi-white-box attack is completed.

A network device controller (104) configured to generate one or more initial Channel State Information, CSI, representative configuration(s) indicating parameters of a CSI representative computation algorithm, transmit the one or more initial CSI representative configuration(s) in one or more first CSI-ReportConfig message(s) to one or more User Equipment(s) (110), UEs, receive a feedback message(s) from at least one of the one or more UEs (110), the feedback message(s) comprising indicators of measurements, perform an evaluation of the measurements and decide if the CSI representative configuration(s) need(s) to be updated, and if so generate an updated CSI representative configuration(s) and transmit the updated CSI representative configuration(s) in one or more second CSI-ReportConfig message(s) to the at least one of the one or more UEs (110).

This application provides a mask layout correction method, system, medium, program product, and terminal based on a combination of membrane computing and the DBSCAN algorithm. The method involves obtaining an initial layout, performing preliminary clustering on the initial layout using a preset clustering method to obtain several partitions and their corresponding density and contour features; obtaining initial density and contour parameters for DBSCAN clustering; iteratively optimizing the initial clustering parameters of the DBSCAN clustering using a preset membrane computing algorithm based on the density and contour features of each partition to obtain the optimal clustering parameters for each partition; performing DBSCAN clustering on each partition based on the corresponding optimal clustering parameters to obtain the core region, boundary region, and sparse region of the initial layout; and correcting the core region, boundary region, and sparse region based on a preset correction strategy to obtain the corrected layout of the initial layout. This improves the accuracy of mask layout correction.

The present disclosure provides a method for scheduling test tasks, relating to the technical field of computer, and the implementation scheme is: obtaining a test task to be scheduled, a test data set, and a state of computing power and bandwidth resources; calculating an algorithm feature vector, a data set feature vector, and a test strategy feature vector based on the test task, the test data set, and the state of computing power and bandwidth resources; constructing a ternary relationship graph based on the algorithm feature vector, the data set feature vector, and the test strategy feature vector; performing feasibility judgment and cost evaluation on each ternary relationship graph; selecting a target ternary relationship graph to generate a task execution scheme based on the feasibility judgment and the cost evaluation; and binding the test task to the matched resource slot and data set for execution according to the task execution scheme, which can realize the integration of algorithm features, data set features, and test strategies into decision-making, can prioritize the coverage of key scenarios, and can improve resource utilization, reduce queuing delay, and reduce data transfer cost.

This application discloses a design method for an atomic-level computing accelerator, belonging to the field of atomic-level computing. The method includes: obtaining the computational characteristics and computational scale range of the target atomic-level computing algorithm to be designed; determining the number of parallel computing units and the number of time-division multiplexing cycles based on the computational characteristics and computational scale range, combined with hardware resource budget; configuring the data input and data output for each time-division multiplexing cycle based on a pipelined design architecture; and, by deploying storage units with a near-memory computing layout, completing the sub-module design of the atomic-level computing accelerator, thereby completing the atomic-level computing accelerator design, in conjunction with the number of parallel computing units and the time-division multiplexing cycle for completing the pipelined design. This application balances computational efficiency and hardware overhead by employing a time-division multiplexing strategy in the atomic-level computing accelerator design, significantly reducing hardware resource overhead, and reducing data movement through a near-memory computing layout, overcoming the memory wall bottleneck and effectively improving the performance of the computing chip.

The invention discloses a torsional vibration signal feature extraction method based on an improved grey goose algorithm. The method comprises the following steps: S1, acquiring an original torsional vibration signal of a compressor main shaft in a working state by using signal acquisition equipment; s2, performing noise reduction processing on the original torsional vibration signal through an MOMEDA algorithm; s3, improving a grey goose optimization algorithm based on a chaotic mapping method; s4, calculating a decomposition number K and a penalty factor of the VMD algorithm based on an improved grey goose optimization algorithm, and obtaining an optimal parameter combination of the decomposition number of the VMD and the penalty factor; s5, performing VMD decomposition on the denoised torsional vibration signal by adopting the optimal parameter combination in the S4 to obtain a plurality of different IMF components; and S6, screening the plurality of different IMF components in the S5, and reconstructing the torsional vibration signal by using the screened IMF components to obtain a reconstructed torsional vibration signal, thereby realizing feature extraction of the torsional vibration signal. According to the method, more accurate torsional vibration signal characteristics can be obtained, and the purpose of accurately analyzing the running state of the compressor is achieved.

The invention relates to the technical field of database encryption, and provides a multi-client sequence uncovering encryption method and system based on block optimization, and the method comprises the steps: carrying out the encryption of a queried message and a query message through employing an expansion Hash calculation algorithm, and obtaining a ciphertext and a token; according to the expanded Hash calculation algorithm, a Hash component is added on the basis of a deterministic attribute maintaining Hash protocol, so that equal judgment and continuous judgment are supported; for the ciphertext and the token, verifying whether the block hash is equal; if all the block Hash are equal, the queried message is equal to the query message; if the block Hash is not equal, bit comparison is carried out, and the size of the queried message and the size of the query message are judged. Two types of judgments can be completed through one verification operation, so that the overall performance can be remarkably improved in a high-concurrency scene.

The present application relates to the field of industrial data analysis, and more particularly to an industrial big data graph computing algorithm, the present application constructs a runtime sequence diagram for industrial equipment by obtaining equipment application data corresponding to a plurality of tasks in a predetermined period of an industrial plant, identifies a heavy operation time domain segment to extract a running state feature corresponding to the heavy operation time domain segment, evaluates a time sequence compactness representation value of the industrial equipment in combination with the running state feature and an average physical distance between the industrial equipment to divide the running state of the industrial plant, collects equipment working features of the heavy operation time domain segment according to the running state, and adaptively evaluates and analyzes the operation scheduling of the industrial plant. The present application improves the accuracy of the running state evaluation of the industrial equipment. At the same time, the present application can adapt to complex scene requirements, dynamically adapt the operation scheduling, realize dynamic balance of the operation efficiency and resource efficiency under different load scenes, and improve the continuity and economy of industrial production.

The application discloses a kind of suspended embankment flowmeter computing system, belong to suspended embankment flowmeter technical field, including;Specifically including main selection type computing program and four auxiliary conversion subprogram;The main selection type computing program is equipped with circular pipeline, rectangular pipeline flow calculation unit, and the integrated wedge flowmeter, venturi flowmeter selection type computing algorithm;The main selection type computing program generates flow-out coefficient correction model according to suspended embankment flowmeter throttling piece multi-working condition test data, and the flow coefficient and differential pressure range are calculated based on flow empty height-diameter ratio h / D or equivalent aperture ratio d / D, and output flowmeter processing size, working condition flow, differential pressure data and differential pressure transmitter debugging parameter;The modularization architecture of the application is adopted single main program is matched with four special auxiliary subprograms, can be simultaneously realized circular pipeline, rectangular pipeline two kinds of pipe cross section accounting, compatible venturi, wedge two kinds of throttling flowmeter selection type, break the limitation that traditional software can only single model, single pipeline calculation.

The application provides a multi-modal large model-based training and examination robot system, equipment and computer program product, which is based on regular training work of a team, takes a training and examination robot, a sensor acquisition equipment and a big data platform as a basis, and proposes a multi-modal data acquisition layer for realizing real-time posture detection and motion capture through an edge computing algorithm, a data processing and analysis layer for completing real-time image processing through localized edge computing in a network-free or electricity-free environment, and an intelligent decision and control layer for completing intelligent decision and evaluation error correction and other system control functions and establishing a training subject rewriting decision mechanism, so as to realize training subject transposition evaluation, realize modal representation consistency through cross-modal collaborative learning middleware, support training and examination operation of the same subject of multiple persons or different subjects of multiple persons, and significantly improve training and examination efficiency.

Systems and methods for utilizing imperfect resource states in a fusion based quantum computing system. First and second entangled quantum states are received and input to a quantum computational algorithm that performs a fusion measurement on the first and second entangled quantum states. It is determined that one of the first or second entangled quantum states have an error that exceeds an error threshold. Responsive to the error exceeding the error threshold, the quantum computational algorithm is dynamically modified such that an alternative process resulting in classical measurement results is performed instead of the fusion measurement. The classical measurement results are stored in a non-transitory memory medium.

Devices, systems, and methods for digital video coding, which includes inter prediction with refinement, are described. An exemplary method of video processing includes determining to use, for a conversion between a current block of a video and a bitstream representation of the video, a first linear optimization model for the conversion using a first coding mode, the first linear optimization model being derived from a second linear optimization model that is used for the conversion using a second coding mode, and performing, based on the determining, the conversion. Another exemplary method of video processing includes determining to use, for a conversion between a current block of a video and a bitstream representation of the video, a gradient value computation algorithm for a bi-directional optical flow tool, and performing, based on the determining, the conversion.

The invention relates to a station transformer fault diagnosis early warning method based on sensing, communication, calculation and storage integration, and the method comprises the steps: constructing an integrated system of real-time sensing, lightweight communication, local calculation and intelligent storage through the deep fusion of voiceprint collection hardware, edge calculation, an AI algorithm and data storage; multi-scene adaptive voiceprint acquisition hardware is utilized to realize real-time sensing of operation acoustic signals, key fault features are extracted in combination with feature engineering and an artificial intelligence algorithm, and an autonomous learning mechanism of edge computing and cloud collaboration is utilized to continuously optimize the fault recognition capability. A whole process solution integrating signal acquisition, feature processing, intelligent diagnosis and dynamic iteration is formed, a station transformer fault acoustic diagnosis and early warning system based on sensing, communication, calculation and storage integration is finally developed, early recognition and early warning of station transformer faults are achieved, and the industry is promoted to change from rule-driven threshold alarm to data-driven intelligent diagnosis. The method has the advantages of high applicability, high efficiency and convenience.

The invention relates to the technical field of automatic control and data processing, in particular to an intelligent cabinet door anti-pinch control method and system based on multi-source sensing fusion, and the method comprises the steps: obtaining the current and speed data in the closing process of an intelligent cabinet door, and determining the resistance drift consistency degree in combination with factory reference data; analyzing the high-frequency oscillation energy ratio of the current data and the change trend correlation between the current and the speed, and comprehensively constructing an environmental state confidence coefficient; and calculating a self-adaptive inertia weight of a particle of the PSO algorithm based on the environmental state confidence so as to drive the PSO algorithm to perform parameter optimization on the SVM classification model, so that the classification model dynamically adapts to the current environment, an optimized SVM model is obtained, and intelligent anti-pinch control is performed on the cabinet door. According to the method, an environment perception and self-adaptive optimization mechanism is introduced, so that resistance abnormity caused by environmental factors and a real person / cargo clamping state can be accurately distinguished, and the accuracy of anti-pinch control is improved.

The application relates to the technical field of production scheduling, and discloses a production scheduling visualization method based on big data. The method first collects multi-dimensional production data such as real-time equipment states, material inventories, personnel skills and order delivery dates, generates a four-dimensional dynamic constraint model through a dynamic constraint modeling algorithm, obtains an initial scheduling scheme through a mixed integer programming and improved genetic algorithm double-driving optimization engine, inputs the initial scheduling scheme into a visualization interaction module, displays the result in a drag-and-drop Gantt chart and detects resource conflicts in real time, and updates the model after the feasibility of the adjusted scheduling scheme is verified through a dynamic recalculation algorithm. The application integrates multi-dimensional data, realizes production scheduling optimization and visualization, can effectively solve production resource conflicts, improves production efficiency, reduces costs and provides an efficient solution for enterprise production management.

The present application relates to the technical field of automatic control and data processing, in particular to a kind of intelligent cabinet door anti-pinch control method and system based on multi-source sensing fusion, the method comprises: obtaining current and speed data in the process of intelligent cabinet door closure, and determine the consistent degree of resistance drift in combination with factory reference data;Analysis of the proportion of high-frequency oscillation energy of current data and the correlation of current and speed variation trend, comprehensive construction environment state confidence;Based on the adaptive inertia weight of particle of PSO algorithm calculated by environment state confidence, to drive PSO algorithm to optimize the parameters of SVM classification model, make classification model dynamically adapt to the current environment, obtain the optimized SVM model, carry out intelligent anti-pinch control to cabinet door.The method can accurately distinguish the resistance anomaly caused by environmental factors and the real pinch / handling state by introducing environmental perception and adaptive optimization mechanism, improve the accuracy of anti-pinch control.

This invention discloses an ontology-state-driven multi-agent adaptation and cooperative control method and system, belonging to the fields of artificial intelligence and intelligent control. The invention constructs an ontology state monitoring module to capture system state changes in real time and generates state change vectors using a difference calculation algorithm. A context compiler maps ontology changes into multi-dimensional context parameters and behavioral weight instructions, achieving dynamic adaptation of agent decisions. A two-stage calibration compilation mechanism processes natural language instructions, combining ontology constraints to complete entity alignment and legality verification. A closed-loop feedback system is established, forming a continuous optimization cycle of perception-decision-execution. This technology significantly improves the adaptive capability of multi-agent systems in dynamic scenarios, ensures the accuracy and security of instruction execution, and is applicable to multiple fields such as intelligent manufacturing and intelligent transportation, possessing strong scalability and industrial-grade practicality.

A computer-implemented method for vehicle impact analysis including obtaining a B-Rep representing an outer surface of a vehicle, the B-Rep having faces and obtaining a radius value for a contact sphere. The method also includes determining one or more two-point-contact curves of the B-Rep for the radius value. The determining includes, for each respective pair of portions of a plurality of pairs of portions of the B-Rep, applying a respective two-point-contact-curve computing algorithm. The computing algorithm is based on the respective pair of portions. The computing algorithm is parallelized over the plurality of pairs of portions. The method forms an improved solution for vehicle impact analysis.

The invention relates to a universal multi-moment finite volume method for compressible multiphase flow of an unstructured grid. According to the method, two types of independent variables, namely a volume integral average value and a point value, are respectively defined on a grid unit and a vertex of the grid unit; the core innovation of the method lies in an updating strategy of a point value: firstly, constructing a vertex reconstruction polynomial based on a reconstruction template; then, on a flux calculation template formed by geometric midpoints of the template, directly adopting a Roe, an HLL or an AUSM and other general Riemann solver to calculate flux; and finally, flux divergence is obtained through least square fitting so as to complete updating. According to the design, the dependence of a traditional method on a specific Riemann solver based on a derivative is broken through, and the compatibility with various mainstream solvers is realized, so that a high-order multi-moment framework is successfully expanded to compressible multiphase flow simulation. According to the method, flux calculation of the volume integral average value and the point value is unified, the algorithm is simple, excellent geometric adaptability is achieved, and high-order precision and high robustness are shown in single-phase and two-phase flow simulation.

The invention provides a personalized privacy computing algorithm negotiation generation method and system. The method comprises the following steps: setting user requirements and parameters of a privacy computing algorithm through a visual interface; generating a preliminary algorithm demand framework according to the set user demand and the parameters of the privacy calculation algorithm; the method comprises the following steps: interacting with a user through an artificial intelligence dialogue module, obtaining an interaction result by the artificial intelligence dialogue module according to an existing algorithm library and a user demand, confirming an algorithm demand framework document, and feeding back the user demand; evaluating whether the demand meets the requirement by combining the interaction result; generating a personalized privacy calculation algorithm meeting the requirement according to the user demand, and carrying out containerization packaging processing on the generated personalized privacy calculation algorithm; and verifying and evaluating the personalized privacy calculation algorithm.