75 results about "Statistical learning" patented technology

A system is provided for dynamic scheduling and optimisation of diagnostic tasks in a networked computing environment. The system associates issue tickets with a diagnostic task matrix comprising probable causes, diagnostic tasks, probability values, outcome expectations, and resource parameters. A task scheduling controller generates optimised task sequences based on task success likelihoods, cost, technician availability, and evidentiary sufficiency. As tasks are completed, outcomes are used to update the diagnostic model, enabling automatic self-improvement. A statistical learning model, such as aBayesian or neural network, refines diagnostic probabilities using historical data. Integration with calendaring systems allows real-time rescheduling based on personnel availability. A graphical interface supports live drag-and-drop reconfiguration of task associations, with immediate propagation of updates to task probabilities and cost metrics. The system thereby enhances resolution speed, accuracy, and resource efficiency across evolving operational contexts.

The invention relates to a rock slope support model construction scheme generation method and device, equipment and a medium. According to the method, a three-dimensional geological model fusing geological information and rock mass parameters is constructed, an initial damage field is obtained in combination with micro-seismic monitoring data and statistical learning inversion, and then a constitutive model capable of reflecting the damage and plastic coupling evolution law is established and calibrated; the model is used for dynamically predicting a spatio-temporal evolution path of a potential slip plane in the excavation process, the supporting opportunity and position are accurately judged based on the stress and damage state in the path, a spatio-temporal sequence scheme is generated, and finally optimal supporting parameters are solved through the multi-objective optimization model. And finally, a set of dynamic support construction scheme capable of actively controlling damage development and giving consideration to safety and economical efficiency is integrated and output, technical spanning from passive reinforcement to active intervention and from static design to dynamic optimization is achieved, and the accuracy and reliability of slope support are effectively improved.

The invention belongs to the technical field of information, and discloses an IT performance index early warning method based on time sequence analysis, and the method comprises the steps: S1, inputting historical data; s2, risk detection; and S3, outputting early warning. Through fusion of statistical learning and machine learning technologies, accurate identification and early warning of three scenes of slow degradation of IT performance indexes, mode switching and capacity bottleneck are realized. The method specifically aims to improve the accuracy of performance index trend identification through a variable-point enhanced segmentation regression algorithm, and help an I T manager to more accurately evaluate the influence and trend of performance changes. Designing a cycle-adaptive double-sample hypothesis testing mechanism, and detecting and identifying switching of different load modes, especially significant changes caused by special or emergency events; and constructing a multivariable autoregressive VAR model, establishing a relation model of performance indexes and capacity indexes, evaluating capacity bottleneck conditions, and performing early warning in time.

The invention discloses a few-sample inductance core surface defect detection method based on model interaction, and belongs to the technical field of machine vision and industrial defect detection. The method comprises the following steps: S1, data acquisition and image preprocessing are carried out, and normal samples, labeled samples and unlabeled samples are constructed; s2, constructing an unsupervised statistical model based on statistical learning; s3, constructing a supervised semantic segmentation model; s4, inputting an inductance core image to be detected into the unsupervised statistical model and the supervised semantic segmentation model at the same time for processing, and generating a segmentation result; s5, detecting result differences are quantified; s6, performing parameter updating on the unsupervised statistical model; s7, generating a pseudo label based on an unsupervised statistical model; s8, carrying out weight updating on the supervised semantic segmentation model; and S9, inputting the processed to-be-detected images into the updated unsupervised statistical model and supervised semantic segmentation model in batches for detection to obtain a detection result, and analyzing and calculating system performance indexes.

The invention discloses an HAZOP analysis method and system based on digital delivery, and relates to the technical field of chemical safety risk analysis, and the method comprises the steps: obtaining to-be-analyzed digital delivery data; carrying out digital delivery model integration and dynamic verification by adopting a topological correlation theory and a rule engine method; performing multi-source process data standardization processing and risk element mapping by adopting a statistical learning and semantic matching method; performing model-driven deviation intelligent generation and causal chain analysis by adopting a topological reasoning and analogue simulation method; carrying out cross-professional collaborative optimization and measure closed-loop tracking by adopting a collaborative filtering and closed-loop control method; and carrying out full-life-cycle data linkage and iterative optimization by adopting a feedback learning and knowledge iteration method to generate a standardized report. According to the method and the system, full-process digitization, intelligentization and full-life-cycle management and control of chemical process risk analysis can be realized, and the precision and the efficiency of HAZOP analysis are improved.

Embodiments are directed to a computer-based tool that can identify an anomalous state of a component in a real-world environment, even if the component experiences gradual and / or seasonal trends. The tool receives data from sensors monitoring a component. The tool uses a trained machine learning model to calculate a predicted behavior of the monitored component. Actual behavior of the component, captured by current sensor readings, is compared to the predicted behavior of the component, calculated by the machine learning model, to compute a divergence. The computed divergence is used by a statistical learning method to determine if the component in the real-world environment is in an anomalous state.

Systems, methods, and apparatus for automatic signal detection in a radio-frequency (RF) environment are disclosed. At least one node device is in a fixed nodal network. The at least one node device is operable to measure and learn the RF environment in a predetermined period based on statistical learning techniques, thereby creating learning data. The at least one node device is operable to create a spectrum map based on the learning data. The at least one node device is operable to calculate a power distribution by frequency of the RF environment in real time or near real time, including a first derivative and a second derivative of fast Fourier transform (FFT) data of the RF environment. The at least one node device is operable to identify at least one signal based on the first derivative and the second derivative of FFT data.

The invention relates to the technical field of biostatistics, and discloses a noise language frequency hearing impairment prediction system based on a GEE model and genetic characteristics, and the system comprises a data collection module which collects follow-up visit data of a worker; the data processing module is used for generating a standardized modeling data set; the feature screening module is used for screening features through a statistical learning method to obtain a key feature subset; the time-varying interaction module is used for constructing a generalized estimation equation model and outputting regression coefficient estimation; the risk prediction module outputs the risk probability and the risk layering result of the individual; the decision support module is used for generating hearing protection suggestions of the individuals; according to the method, the generalized estimation equation model is constructed to process follow-up data of occupational noise exposure workers, so that the accuracy and the stability of language frequency hearing loss risk prediction are improved, a basis is provided for formulating a personalized hearing protection scheme and recommending a proper hearing protection device, and the method is suitable for popularization and application. And the transformation of occupational hearing loss from passive treatment to active prevention is facilitated.

The invention discloses a user self-filling washing requirement and standard process difference verification method, and particularly relates to the technical field of life, and the method comprises the steps: obtaining a corresponding standard process parameter vector from a clothes feature vector through a standard process retrieval algorithm based on a process knowledge base, constructing a knowledge base secondary retrieval index, and traversing the process knowledge base; and obtaining a candidate set meeting the constraint, screening out a record with the highest score, and obtaining a recommendation process and a security boundary. The method is realized based on a standard parameter vector, a rule engine and a statistical learning method, can be conveniently integrated into an existing internet clothes washing platform or a traditional clothes washing management system, can be flexibly expanded according to standard process libraries of different enterprises, and is easy to integrate and expand.

Systems, methods, and apparatus for automatic signal detection in a radio-frequency (RF) environment are disclosed. At least one node device is in a fixed nodal network. The at least one node device is operable to measure and learn the RF environment in a predetermined period based on statistical learning techniques, thereby creating learning data. The at least one node device is operable to create a spectrum map based on the learning data. The at least one node device is operable to calculate a power distribution by frequency of the RF environment in real time or near real time, including a first derivative and a second derivative of fast Fourier transform (FFT) data of the RF environment. The at least one node device is operable to identify at least one signal based on the first derivative and the second derivative of FFT data.

The invention provides an intelligent detection system and method for hidden cracks and fragments of a silicon wafer, and the system comprises an imaging unit, an image processing unit and a control and communication unit, and is used for obtaining a surface image of the silicon wafer located on a conveying belt; the image processing unit is in communication connection with the imaging unit and is used for receiving and processing the surface image, the image processing unit comprises a processor, and the processor is configured to automatically generate an effective detection area excluding an edge area and an electrode area in the surface image based on pre-stored silicon wafer size information and electrode distribution information; the processor is used for segmenting the image in the effective detection area based on a gray threshold range determined through statistical learning, extracting a suspected defect area, performing feature extraction on the suspected defect area, and performing defect classification and judgment according to preset defect feature parameters; and the control and communication unit is connected with the image processing unit, so that efficient and accurate subfissure and fragment detection is realized on the premise of not changing the structure of a production line.

The application relates to the technical field of hydraulic engineering, and discloses a soil and rock dam dam-break probability prediction method, which comprises the following steps: acquiring basic cause variables and state conversion variables leading to soil and rock dam dam-break, adopting a fixed threshold method to perform discretization processing, and generating discretization standards of the variables; a one-dimensional dam-break flow simulation model is constructed; based on the discretization standards, a data set covering multiple grades of flood scenarios is generated; a three-layer Bayesian network model is constructed; the data set is input into the three-layer Bayesian network model for training; a Bayesian smoothing method is adopted to calculate the conditional probability of each node in the three-layer Bayesian network model, so as to correct model parameters; finally, the trained three-layer Bayesian network model is input into the variable data of a to-be-tested soil and rock dam, so as to realize dam-break probability prediction and risk grade evaluation; through fusion of a physical mechanism and statistical learning, the dam-break prediction probability is quantified and improved, and high-precision dam-break risk prediction is realized.

The invention belongs to the technical field of power system dynamic safety analysis and renewable energy source grid connection, and provides a method for determining the permeation limit of high renewable energy sources under multi-dimensional stability constraint. The method takes physical mechanism depth modeling-multi-dimensional stability coupling theory-intelligent data driven screening as a core, constructs a quantitative correlation model of frequency stability, transient stability and small disturbance stability through rigorous theoretical derivation, proposes a global-region double-layer dynamic inertial constraint mechanism, and combines an AI-driven critical scene screening and clustering method to obtain a critical scene clustering algorithm. And finally determining the safe RE penetration limit. According to the method, the limitation of traditional single-dimension evaluation and static constraint is broken through, all theoretical derivation is based on the dynamic characteristics and the statistical learning principle of the power system, moderate data verification is assisted, and a complete theoretical system and an engineering tool are provided for RE safety integration of the low-inertia power grid.

Systems, methods and apparatus are disclosed for automatic signal detection in an RF environment. An apparatus comprises at least one receiver and at least one processor coupled with at least one memory. The apparatus is at the edge of a communication network. The apparatus sweeps and learns the RF environment in a predetermined period based on statistical learning techniques, thereby creating learning data. The apparatus forms a knowledge map based on the learning data, scrubs a real-time spectral sweep against the knowledge map, and creates impressions on the RF environment based on a machine learning algorithm. The apparatus is operable to detect at least one signal in the RF environment.

Systems, methods, and apparatus for automatic signal detection in a radio-frequency (RF) environment are disclosed. At least one node device is in a fixed nodal network. The at least one node device is operable to measure and learn the RF environment in a predetermined period based on statistical learning techniques, thereby creating learning data. The at least one node device is operable to create a spectrum map based on the learning data. The at least one node device is operable to calculate a power distribution by frequency of the RF environment in real time or near real time, including a first derivative and a second derivative of fast Fourier transform (FFT) data of the RF environment. The at least one node device is operable to identify at least one signal based on the first derivative and the second derivative of FFT data.

The invention provides a sea surface wind speed deviation correction method and system based on statistical learning and physical constraint, and belongs to the field of satellite ocean remote sensing and meteorological data reanalysis. According to the method, the technical problems that the correction result of the existing multi-source satellite wind speed data is unstable in statistics and unreasonable in physics due to inherent system errors of a sensor and sparse samples in a high-wind-speed interval, and an accurate and reliable correction result cannot be generated in a full-wind-speed range (especially under a high-wind-speed condition) can be solved. Specifically, quality control and space-time matching are carried out by acquiring multi-source satellite wind speed data and high-precision reference wind field data, an initial deviation correction lookup table is constructed by adopting a double-reference partitioning strategy based on satellite observation wind speed and reference wind field wind speed, statistical stabilization processing is carried out on the lookup table by utilizing a hierarchical Bayesian contraction estimator, and the initial deviation correction lookup table is obtained. The physical continuity of a wind speed-deviation curve is improved through an adaptive LOESS smoothing algorithm, and an exponential decay function based on a turbulence energy spectrum theory is introduced.

The invention discloses a battery monitoring and early warning method and device based on parameter space statistical learning, and relates to the field of electrochemical energy storage abnormity monitoring, and the method comprises the steps: constructing a multi-dimensional working condition feature vector and a monitoring index of a battery, and discretizing a multi-dimensional working condition space into a subspace unit with a unique identifier; based on historical normal operation data, performing statistics on a sample mean value and a standard deviation of the monitoring indexes in each subspace, and establishing a historical statistical distribution model; in real-time monitoring, positioning the corresponding subspace according to the current working condition, and expanding and retrieving the parent space step by step if the data volume is insufficient; when the data is sufficient, the deviation degree of the current monitoring value is calculated, an abnormal point is determined, early warning is carried out through the abnormal frequency in a sliding window, and meanwhile self-adaptive updating is carried out on a statistical model. According to the method, the error rate of abnormal recognition and early warning of the battery unit is reduced, the adaptive capacity and interpretability of the statistical distribution model are improved, the judgment standard can be autonomously optimized and updated, and the self-learning capacity of the system is improved.

1. The name of the design product: college student focused habit forming graphical user interface of mobile phone. 2. The use of the design product: for running programs and displaying graphical user interfaces. 3. The design points of the design product: in the graphical user interface. 4. The picture or photo that best indicates the design points: front view. 5. The use of the graphical user interface: to improve the learning efficiency and self-discipline of college students, by recording and counting the learning time, the focused results can be seen directly, and the formation of learning habits is promoted. 6. The change state of the graphical user interface: the front view is the initial interface of the college student focused habit forming application, clicking the "concentrate clock" icon below the front view enters change state figure 1, clicking the round icon at the upper right corner of change state figure 1 enters change state figure 2. 7. Other circumstances that need to be explained: "XXX" in the view represents replaceable text and / or numbers and / or letters and / or symbols, and the gray block part in the view is the content screen.

Systems, methods and apparatus are disclosed for automatic signal detection in an RF environment. An apparatus comprises at least one receiver and at least one processor coupled with at least one memory. The apparatus is at the edge of a communication network. The apparatus sweeps and learns the RF environment in a predetermined period based on statistical learning techniques, thereby creating learning data. The apparatus forms a knowledge map based on the learning data, scrubs a real-time spectral sweep against the knowledge map, and creates impressions on the RF environment based on a machine learning algorithm. The apparatus is operable to detect at least one signal in the RF environment.

The invention relates to the technical field of hydraulic engineering, and discloses an earth and rockfill dam break probability prediction method, which comprises the following steps of: obtaining a foundation dependent variable and a state conversion variable which cause earth and rockfill dam break, performing discretization processing by adopting a fixed threshold method, and generating a discretization standard of each variable; constructing a one-dimensional dam break water flow simulation model, and generating a data set covering a multi-level flood scene based on a discretization standard; constructing a three-layer Bayesian network model; inputting the data set into a three-layer Bayesian network model for training, calculating the conditional probability of each node in the three-layer Bayesian network model by adopting a Bayesian smoothing method so as to correct model parameters, and finally, after the trained three-layer Bayesian network model is generated, inputting each variable data of the earth and rockfill dam to be tested, therefore, dam break probability prediction and risk level evaluation are realized. By fusing a physical mechanism and statistical learning, the dam break prediction probability is quantified and improved, and high-precision dam break risk prediction is realized.

A method includes obtaining information representing a current state of communication with a user equipment (UE) performed using one or more beams. The method also includes comparing the information to statistical historical state information to determine one or more best next narrow beam candidates. The method further includes performing a beam search using the one or more best next narrow beam candidates in order to select a next narrow beam. The method also includes communicating with the UE using the selected next narrow beam.

This invention relates to a method and system for real-time monitoring of the additive manufacturing (AM) building process of a three-dimensional (3D) product generated by a 3D building device. The building process comprises alternately distributing a material on a substrate by a coating mechanism and forming a layer by fusing a portion of the material according to a planned geometry. An imaging device captures image data for each build layer, including a pair of images representing the distributed material prior to layer formation and the corresponding build layer after formation. A processor processes the image data by performing a localized-image evaluation to detect local surface defects before and after layer formation and determine a localized-image failure metric, and / or by performing a whole-image evaluation using statistical learning models to determine a whole-image failure metric. Based on the comparison of the failure metrics with reference values, each layer is categorized as critical or non-critical. The method further includes determining a forward-looking failure risk indicator using the evaluation results and a defined number of most recent, to predict whether the building process is trending toward failure.

A method for using a Gaussian Process-based algorithm to approximate an optimal stopping of a time series that corresponds to a sequence of events is provided. The method includes: receiving information that relates to an event sequence; estimating, based on the received information, a first potential reward that is obtained by stopping the event sequence at a first time, and a set of respective second potential rewards that are obtained by stopping the event sequence at corresponding times; and determining, based on the estimated first and second potential rewards, an optimal time for stopping the event sequence. The event sequence may include a numerical sequence that is modeled as a statistical learning method via a Gaussian Process (GP) function and / or a deep GP function that indicates a probability density distribution of the items in the numerical sequence over a predetermined time interval.

Systems, methods, and apparatus for automatic signal detection in a radio-frequency (RF) environment are disclosed. At least one node device is in a fixed nodal network. The at least one node device is operable to measure and learn the RF environment in a predetermined period based on statistical learning techniques, thereby creating learning data. The at least one node device is operable to create a spectrum map based on the learning data. The at least one node device is operable to calculate a power distribution by frequency of the RF environment in real time or near real time, including a first derivative and a second derivative of fast Fourier transform (FFT) data of the RF environment. The at least one node device is operable to identify at least one signal based on the first derivative and the second derivative of FFT data.

The application discloses a user travel evaluation sentiment analysis method and device. The method comprises the following steps: preprocessing a user travel evaluation text to obtain a feature matrix; inputting the feature matrix into a trained random forest sub-model to output a first sentiment probability; inputting the feature matrix into a trained LSTM model with attention mechanism to output a second sentiment probability; fusing the first and second sentiment probabilities by weighting to obtain a third sentiment probability; inputting the feature matrix, the first sentiment probability and the second sentiment probability into a generator to output a sentiment analysis report; determining the authenticity of the sentiment analysis report by using a discriminator combined with a confidence threshold; obtaining a fourth sentiment probability; and fusing the third and fourth sentiment probabilities to obtain a final sentiment probability. The method disclosed in the application fuses a hybrid architecture of statistical learning and deep learning, and introduces an adversarial learning mechanism to realize dynamic optimization of sentiment analysis results, so that the sentiment judgment precision is improved through multi-level model cooperation.

The invention belongs to the field of image processing, and discloses an underwater image quality evaluation method and system based on visual contrastive analysis and brightness-chrominance statistical learning, which enable non-reference quality evaluation to have class reference capability by generating a pseudo original image corresponding to a distorted image to be detected, and remarkably improve the effectiveness of quality difference measurement. By comparing and analyzing the pseudo original image and the distorted image to obtain the first perception quality difference feature, the real perception change of underwater distortion can be described, and the accuracy of perception difference measurement is improved. Second perception quality difference features are calculated based on the similarity of the two, so that the evaluation model can comprehensively consider the structural consistency and the detail degradation degree, and the final quality judgment is more fit for the visual perception rule. According to the method, the accuracy and robustness of no-reference underwater image quality evaluation are greatly improved, and the method is suitable for more engineering application scenes.

The invention relates to a method for predicting the content of elements in coal based on LIBS spectral feature optimization and machine learning, which comprises the following steps of: converting a preprocessed spectrum into a two-dimensional spectral matrix, matching a peak wavelength point of the two-dimensional spectral matrix with an NIST standard atomic spectrum database, identifying spectral lines belonging to target elements, and determining the content of the elements in the target elements according to the spectral lines. A characteristic wavelength set stably appearing in all coal sample spectrums is screened out to serve as candidate characteristics; further screening out a core characteristic wavelength subset which is most relevant to the content of the target element and has the minimum redundancy by applying a plurality of characteristic selection methods; and then selecting an optimal model combination by adopting a plurality of machine learning regression algorithms. According to the method, through a two-stage screening strategy combining physical spectral line identification (NIST matching) and statistical learning feature selection, irrelevant and redundant spectral information is eliminated to the greatest extent, anti-interference core features directly related to the target element content are reserved, and the quality of model input data is improved from the source.

The embodiment of the application provides an intelligent ore-prospecting method and device based on multi-modal data and a control theory model, which comprises the following steps: obtaining a multi-modal data body by fusing and aligning multi-source heterogeneous data of a target mining area; extracting key geological slow variables from the data body as system order parameters based on the Haken slave principle and sparse statistical learning, and classifying the key geological slow variables into resistance and driving force control variables for gridding; then, applying the Thom cusp catastrophe theory to calculate a mutation discriminant value in the whole area; finally, intelligently delineating and optimizing an ore-prospecting target area through anomaly area identification, uncertainty analysis and Bayesian updating driven by real drilling data, and a high-confidence exploration target. The application can improve the accuracy and efficiency of deep ore prospecting.

The invention relates to an AI-based medical-care integrated data management method and system, electronic equipment and a storage medium. The method comprises the following steps: acquiring multi-source heterogeneous data of a target object; performing data cleaning on the multi-source heterogeneous data based on a hierarchical cleaning strategy to obtain target data; performing feature alignment on the feature vectors in the target data based on a metric learning algorithm to generate a fused feature vector; reasoning and evaluating the fusion feature vector based on a hybrid expert model to obtain an auxiliary diagnosis result; wherein the hybrid expert model integrates a traditional Chinese medicine rule engine and a western medicine statistical learning engine. Therefore, the quality problem of the multi-source heterogeneous data is solved in a targeted manner through a hierarchical cleaning strategy, so that the interference of invalid data is reduced, and the accuracy and integrity of the data are ensured; based on a feature alignment algorithm of metric learning, a cross-domain mapping relation can be established, multi-dimensional data form complementation, a fusion feature vector is finally generated, and the data representation capability is enhanced.

The invention relates to the technical field of ecological hydrological modeling and artificial intelligence crossing, in particular to a multi-source satellite data driven irrigation area ecological hydrological basic model construction method, which comprises the following steps: acquiring multi-source satellite data and ecological hydrological related data of a target irrigation area; preprocessing the multi-source satellite data and the ecological hydrological related data, and interpolating missing measurement data to generate complete ecological hydrological data; according to the method, time sequence satellite general characterization generated by multi-source satellite data and physical variable embedding of ecological hydrological data are fused, primary ecological hydrological general characterization is generated, an irrigation area ecological hydrological basic model is constructed in combination with a self-supervision task, unified ecological hydrological general characterization is output, and ecological hydrological variable estimation is carried out. Therefore, the problems that in related technologies, due to dependence on statistical learning and lack of physical constraints, result time sequences are discontinuous or do not conform to physical laws, multi-source data fusion is insufficient, and high-precision estimation of ecological hydrological elements is difficult to achieve are solved.