54 results about "Error reduction" patented technology

The invention discloses a multi-frequency multi-mode signal multi-path error weakening method, and relates to the technical field of precision positioning, and the method comprises the steps: constructing a multi-frequency multi-mode non-combination precision single-point positioning observation equation, separating multi-path errors in pseudo-range and phase observation values through the equation, and forming residual data; performing quality control on the residual error, and eliminating an abnormal value; feature extraction and judgment are carried out based on residual data, wherein time repeatability features, spatial distribution features and trend complexity features are extracted; selecting a correction model according to a feature judgment result, wherein the correction model comprises a fixed star daily filtering model, a multi-path hemispherical graph model and a trend surface analysis multi-path hemispherical graph model; and generating a multi-path error correction amount through a selected model, correcting an original observation value, and outputting a positioning result. The method solves the problem that the positioning precision is reduced due to multi-path errors, can effectively weaken the multi-path errors, improves the positioning precision and stability, adapts to a complex environment, and reduces data processing redundancy.

The invention discloses a power grid frequency balance intelligent control system based on a distributed architecture, relates to the technical field of power grid control, and is used for solving the problems that the existing WLS / UKF and other methods are generally assumed to be good in synchronization, are insensitive to heteroscedasticity time delay and missing measurement, and are difficult to ensure robustness under real communication and equipment conditions. Through a through link, under the real clock misalignment, time delay jitter and packet loss conditions, measurement of a unified time axis and quality quantification are realized, and frequency estimation bias and uncertain transmission are significantly reduced. Reconstruction, filtering and triggering are penetrated with the same confidence degree, and abnormal measurement suppression and effective information utilization are improved; the fusion efficiency and timeliness of asynchronous arrival data are improved by utilizing graph prior and information domain incremental updating; and regional measurement abnormity and real power imbalance are discriminated through double-model causality, and error event reporting is reduced.

Systems and techniques that facilitate quantum noise suppression are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory that can execute the computer executable components stored in memory. The computer executable components can comprise an error detection component that determines one or more portions of a quantum circuit susceptible to noise errors; and an error reduction component that compiles an inverse of the noise errors into the quantum circuit based on context of the quantum circuit and the one or more determined portions of the quantum circuit.

Systems and techniques that facilitate quantum noise suppression are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory that can execute the computer executable components stored in memory. The computer executable components can comprise an error detection component that determines one or more portions of a quantum circuit susceptible to noise errors; and an error reduction component that adds one or more dynamical decoupling sequences to the one or more determined portions of the quantum circuit wherein the one or more dynamical decoupling sequences are determined based on context of circuit layers of the quantum circuit.

The present invention provides a computer system and a computer-implemented method for adaptive quality management in artificial intelligence (AI) processing. The system comprises a memory unit storing machine-readable instructions and a processor configured to execute the instructions to store, in a persistent correction memory, records of prior corrections to AI outputs; retrieve a correction for a current input based on similarity between the input and stored records; generate, by an audit-trail module, a tamper-evident record of the correction and its application; schedule, by a predictive resource manager, AI workloads in dependence on service-level constraints, carbon-intensity data, and quality metrics; and update, by a feedback controller, retrieval and scheduling parameters based on effectiveness information. The processor thereby enables the persistent correction memory, audit-trail module, predictive resource manager, and feedback controller to operate as a closed-loop adaptive system that reduces recurrence of AI errors, ensures verifiable auditability, and optimizes computational efficiency.

The invention relates to an input adaptive precision Cordic algorithm implementation method and device. The method comprises the following steps: obtaining truth values of input x and y and comparing the truth values; performing most significant bit estimation on the truth value with the larger truth value, and obtaining a corresponding shifting mode; shifting the truth values of the input x and y based on the queried shifting mode; and inputting the complemented input x and y into the Cordic operation unit. According to the invention, the spare bit width in the input data is fully utilized to reduce or avoid the occurrence of error iteration, so that the occurrence of error iteration can be reduced to the greatest extent and the accuracy of the output result can be improved.

In a non-volatile memory device, the amount of delay along an I / O path for transferring received data to a sense amplifier and the amount of delay along a clock path to the sense amplifier can differ. To compensate for this differential delay, a replica of each of the I / O path and the clock path can be included on the memory device. The replica circuits can be used to determine this differential delay between the two paths. To compensate for this difference, an additional delay can be introduced into one or both of the paths, such as in the I / O path, so that any difference in propagations times can be reduced.

The invention discloses a target data selection method based on an approximate Shapley value, electronic equipment and a medium. The target data selection method comprises the steps of obtaining a ratio of a source data set to a target utility, and calculating a total utility value and a target utility value. And screening to obtain an extended sample and a feature set. And sample optimization: carrying out Monte Carlo sampling on the expanded sample set to generate random arrangement, forming a plurality of third subsets and calculating utility values of the third subsets. And after a sample Shapley value is calculated, selecting a subset with the minimum error to carry out iterative sample replacement. And if the error reduction degree is smaller than a second threshold value, re-sampling, otherwise, taking a subset of which the error is smaller than a third threshold value as an initial candidate. And if the final error still exceeds the tolerance threshold, turning to feature optimization. And feature optimization: the process is similar to sample optimization, and feature iteration and replacement are guided through a Shapley value. And alternately optimizing the sample subsets and the feature subsets until the utility value error of the combined candidate set (the candidate sample subsets + the candidate feature subsets) is smaller than an error tolerance threshold.

The invention describes a method (100) for reducing error in the determination of an observable of a quantum system in a quantum state on a noisy quantum computer platform, comprising the following steps: • dividing (101) an observable operator into series of base operators; • providing (102) calibration operators; • measuring (103) the calibration operators, comprising determining (104) noisy measurement results (1040) and gate-error-reduced measurement results (1050) of the calibration operators; • ascertaining (106) a calibration function (1060) for mapping the noisy measurement results (1040) to the gate-error-reduced measurement results (1050) of the calibration operators; • determining (107) noisy measurement results (1070) of the series of base operators; • determining (108) error-reduced measurement results (1080) of the series of base operators by mapping (109) the noisy measurement results (1070) by means of the calibration function (1060); • determining (110) the observable by combining the error-reduced measurement results (1080) of the series of base operators.

The application provides a work order processing method and device, the method comprising: obtaining work order data; extracting fault information from the work order data to obtain the fault information; matching the fault information with work order instances in a previously established fault feature database to obtain matched work order instances; wherein the fault feature database is established based on work order instances and corresponding processing strategies; and calling the corresponding processing strategies and executing according to the matched work order instances. The application extracts fault information from the obtained work order data to clearly define the core content of the fault, remove irrelevant information interference, improve the accuracy and efficiency of subsequent processing, matches the extracted fault information with work order instances in the fault feature database to quickly locate the verified processing strategy corresponding to the matched work order instances, improves the accuracy of work order processing, reduces the possibility of incorrect processing, reduces the time and cost of starting from scratch to analyze and solve problems, and improves the speed and success rate of fault processing.

The invention provides a power transmission and transformation project cost control line measurement and calculation generation method, and relates to the technical field of power transmission and transformation project cost control. Step 2, feature extraction and normalization; 3, applying a normal distribution algorithm; 4, applying a normal distribution algorithm; and 5, generating a result. Through a normal distribution algorithm, a big data structured data mode, data cleaning and feature extraction, an efficient and scientific cost control line generation method is effectively provided for the power transmission and transformation project of the Guangzhou bureau, automatic generation, cost management optimization, accuracy and scientificity improvement, manual error reduction and project implementation guarantee are realized, and the method has significant application value.

The invention relates to the technical field of community management, in particular to a community management method and system based on artificial intelligence. The method comprises the following steps: obtaining an owner question content; analyzing the question content of the owner, and determining a question item; based on the question item, utilizing artificial intelligence to obtain item data; summarizing project bills according to the project data; and checking the project bill to obtain and display a final project bill. The problems of delay and fragmentation of manual association of data sources are eliminated, the integrity and consistency of project data are ensured, data missing or conflicts are reduced, and traceability is enhanced. The problems of error proneness and static account division depending on manual summarization are avoided, the accuracy and dynamic adaptability of bills are improved, and the error rate is reduced. The project bill is checked, and the final project bill is obtained and displayed, so that the problems of omission and low efficiency of manual checking are avoided, the accuracy and the displayability of the final project bill are ensured, real-time and safe publicity is realized, the transparency is improved, and the owner trust is enhanced.

Methods and systems are provided for performance optimization of object grouping patterns in storage devices. The methods include receiving object IOs for a target device; grouping the object IOs using a first plurality of input parameters; associating tracking parameters with the first plurality of input parameters and performance parameters corresponding to the first plurality of input parameters; storing first data entries comprising the tracking parameters, the first plurality of input parameters, and the performance parameters in a database; extracting a plurality of data entries from the database, the plurality of data entries including the first data entries; training a training model using one or more of the plurality of data entries; cross-validating the training model to determine a degree of error reduction of the training model; performing a model check to compare the training model to an inference model; and updating the inference model based on the model check.

The embodiment of the invention provides a small sample anti-fraud classification method and device, a medium and a product, and relates to the technical field of artificial intelligence. The method comprises the steps of constructing intra-class semantic features and inter-class difference features based on a preset task framework of small sample learning; determining an anti-fraud classification network model according to the intra-class semantic features and the inter-class difference features; and according to the network model, performing anti-fraud classification identification on a to-be-classified text. According to the scheme, the preset task framework based on small sample learning does not need large-scale labeling, when a category is newly added, only a small number of samples need to be supplemented according to the framework to be fused into an existing model, an overall data system does not need to be reconstructed, the expansibility is greatly improved, through intra-class and inter-class feature learning, the distinction degree of similar fraud classes is enhanced, wrong classification is reduced, and the classification efficiency is improved. The classification accuracy is improved, general fraud features can be migrated to a new scene, and the generalization ability of the new fraud scene is improved.

The present invention provides a robot control method and system based on a large language model. This system employs a dual large language model evaluation mechanism. The large language model of the code generation and modification module uses Claude 3.5sonnet to generate control code, which is then evaluated and optimized using OpenAI's GPT-4O evaluation model. This reduces the risk of difficulty identifying problems during evaluation due to the homogeneity of the same module, thereby improving the quality, safety, and security of the generated code. In addition to the dual large language model evaluation mechanism, the system incorporates safety prompts into the prompts of the generated model and provides generation template examples. This restricts operation within a specific framework, reduces the likelihood of errors, and further ensures the standardization and security of the generated code. Furthermore, a control verification module constructs a simple simulation environment after the control code is generated, enabling early screening of the generated code for syntax errors and logical vulnerabilities, further reducing potential safety hazards.

The invention provides a tool bar strain measurement device and a machining error reduction method thereof, and relates to the technical field of numerical control lathe machining. The tool bar strain measuring device comprises a tool bar, a sensor mounting groove is formed in the tool bar, a strain sensor is fixed in the sensor mounting groove, a signal transmission channel is arranged at the tail of the tool bar and used for being connected with the strain sensor and a data acquisition device, and the data acquisition device is used for collecting and processing strain signals in real time. Tool bar strain data of the strain sensor are acquired in real time through the data acquisition device, whether machining errors exist or not is quickly judged, and cutting parameters are dynamically adjusted. Machining errors caused by cutting force can be remarkably reduced, universality is high, installation is easy and convenient, the method is suitable for various working conditions, and the machining precision of the numerical control lathe can be effectively improved.

The invention discloses a field expert-based machine question and answer large model illusion question mitigation method, which comprises the following steps: S1, expert field identification: generating a declaration basis for initial reasoning and judgment through a large language model, and identifying experts involved in declaration through a field expert large language model; s2, obtaining a final evidence, identifying a key entity in the statement through a domain expert big language model, taking the key entity as an external evidence for query and retrieval, and selecting an external evidence useful for analysis of the statement as the final evidence; and S3, verifying correctness, comparing the declaration with the final evidence through a domain expert big language model, analyzing the correctness of the declaration, and giving a declaration modification suggestion, so that generation of wrong reasoning is reduced, and the reasoning accuracy is improved.

The invention discloses a large model context error management method and system based on regression testing, belongs to the technical field of artificial intelligence, and aims to solve the technical problems of repeated errors, lack of error learning ability and imperfect error prevention mechanism existing in a large model at present. Comprising the following steps: constructing an error history library based on a triple data structure corresponding to historical error data; performing similarity analysis on the current user input and historical user input in an error historical library; generating a test case based on a historical user input similar to the current user input; optimizing the current user input to obtain optimized user input; and establishing an error reduction rate evaluation index, comparing error occurrence rates before and after user input optimization through an A / B test framework, and adaptively optimizing a similarity judgment standard, a weight parameter and an error avoidance instruction template based on a feedback result.

The invention discloses a pulmonary nodule sub-voxel volume VDT method based on deep learning, and aims to solve the problem of adjustable mapping of depth model probability output to physical volume and the problem of deviation introduced by anisotropic voxels and reconstruction kernels. According to the method, direction-conditioned spatial variable point spread function distribution self-supervised estimation, edge spread function and modulation transfer function consistency constraint, global mapping and local residual error calibration of two-segment conditional flow under monotonous bounded constraint, and occupancy rate quantile output are carried out; and in combination with subvoxel integration and quality control closed-loop trigger conservative expansion and artificial recheck, the technical effects of cross-device and acquisition protocol robust volume and VDT point estimation and interval, uncertainty quantization and error reduction are realized.

This application relates to a method, electronic device, and storage medium for locating errors in a logic system design. The method includes: testing the logic system design using multiple test cases; dividing the multiple test cases into a pass case group and a fail case group based on the test results, the test results including coverage data of the multiple test cases; determining multiple key features based at least on the coverage data of the test cases in the fail case group; and locating the error in the logic system design based on the multiple key features. This method can reduce error location time and accurately pinpoint the source of errors in the logic system design.

The application relates to a knowledge graph multi-hop question answering method and system based on a contrast learning relationship representation, which comprises the following steps: for a KG subgraph corresponding to a question, a corresponding relationship representation is trained based on a contrast learning and a negative sampling mechanism, that is, an encoder is obtained; the relationship is encoded through the encoder, integrated processing is conducted by using a KG context processor, a correlation score for multi-hop reasoning is determined, and a weighted subgraph is obtained; a text classification model is used to predict the number of hops of the question, and an adaptive beam search method is used to search for an optimal weighted chain on the weighted subgraph, and then the answer corresponding to the question is returned. Compared with the prior art, the application can conduct fine multi-hop reasoning on a knowledge graph containing various relationships, effectively find an optimal reasoning chain, reduce error propagation and provide interpretability.

Generally, the present disclosure is directed to systems and methods of quantizing a database with respect to a novel loss or quantization error function which applies a weight to an error measurement of quantized elements respectively corresponding to the datapoints in the database. The weight is determined based on the magnitude of an inner product between the respective datapoints and a query compared therewith. In contrast to previous work, embodiments of the proposed loss function are responsive to the expected magnitude of an inner product between the respective datapoints and a query compared therewith and can prioritize error reduction for higher-ranked pairings of the query and the datapoints. Thus, the systems and methods of the present disclosure provide solutions to some of the problems with traditional quantization approaches, which regard all error as equally impactful.

The application belongs to the technical field of neural network model training, and discloses a neural network model training method and system based on an improved random configuration algorithm, which comprises the following steps: under the condition of reducing the root mean square error of the neural network model output, screening suitable neurons as candidate hidden layer nodes through the inequality constraint condition of the random configuration algorithm; screening K neurons with the fastest training error reduction from the candidate hidden layer nodes, and selecting the neuron least related to the previous L-1 hidden layer nodes from the selected K neurons as the optimal hidden layer node; calculating the output weight through the least square method, and updating the structure of the random configuration network; and judging whether the network structure is completed by using the maximum allowable number of hidden layer nodes and the maximum allowable output error. The application can reduce the overall calculation amount of the algorithm while ensuring the prediction accuracy, and is suitable for application scenarios with high real-time requirements.

The invention relates to a point cloud thermal prediction method for a large-scale unstructured 2.5 D integrated circuit. The network comprises three core modules: an adaptive multi-path coupling diffusion module (AMD), a wavelet-based fine-grained recovery module (WFR) and a thermal sensing material expert hybrid adapter (TA-MoME). The AMD module adaptively learns interaction of a thermal diffusion path through an attention mechanism based on serialized gating; the WFR module recovers fine-grained thermal gradient features by using a high-frequency wavelet domain enhancement technology; and the TA-MoME adapter dynamically routes the material specific expert to adapt to the heat distribution difference between the heterogeneous materials. Particularly, the framework also introduces a continuous learning mechanism with efficient parameters to ensure that when the model adapts to a new geometric scale, catastrophic forgetting can be prevented, and cross-scale adaptation and mobility can be realized. Experimental results show that in 80K-scale point cloud prediction, compared with an existing FSA-Heat method, the average precision index is remarkably improved (errors are reduced by about 78% or above), compared with commercial COMSOL, acceleration of 147 times is achieved, and the generalization ability for larger-scale nodes (0.4 M) and zero samples without geometrical shapes is shown.

It is already known that quantum computers can be used to simulate materials and molecules. However, quantum computers are error-prone and exhibit intrinsic noise, which has so far made the real technical application of quantum computers impossible. Approaches are already known from the prior art which, despite the error susceptibility, allow meaningful simulations of quantum mechanical systems to be created, but the errors still exist. Building on this, the invention now makes it possible to reduce the errors and to include the errors as part of the simulation. In addition, the invention makes it possible to inhibit the effect of intrinsic noise. This further improves the technical applicability of quantum computers for simulating materials and molecules.

A branch predictor predicts a first outcome for a first branch in a first block of instructions. Fetch logic fetches instructions for speculative execution along a first path indicated by the first outcome. In response to taking the first predicted outcome, information representing a remainder of the first block is stored. In response to not taking the first branch instruction, the branch predictor is restarted based on the remainder block. In some cases, an address of the first block is used as an index into a branch prediction structure to access an entry corresponding to a second block along a speculative path from the first block. A corresponding set of instances of branch condition logic are used to simultaneously predict outcomes for branch instructions in the second block, and in response to a misprediction, the predicted outcomes are used in combination with the remainder block to restart the branch predictor.

This invention discloses a method, apparatus, device, and computer-readable medium for automated testing, relating to the field of big data intelligent analysis technology. One specific embodiment of the method includes: modifying an error code enumeration class file according to updated error codes, and pushing the modified error code enumeration class file, wherein the error code enumeration class file is obtained from a data warehouse; compiling and packaging the modified error code enumeration class file, and sending the compiled error code enumeration class file package to a code server; the code server pushing the compiled error code enumeration class file package to perform automated testing using the updated error codes. This embodiment can reduce error code referencing errors and achieve automated testing.