21 results about "Longitudinal data" patented technology

The invention discloses a power load probability prediction method and system based on a neural network quantile regression model and multiple linear regression, and belongs to the technical field of power system load prediction. The method comprises the following steps: obtaining standardized data by using a longitudinal data analysis method; identifying key influence factors of the power load in the standardized data based on a Pearson correlation analysis method, and constructing a factor analysis model to quantify influence weights of the key influence factors on the power load; constructing a neural network quantile regression model based on seasonal trend decomposition to fit the key influence factors with different influence weights to obtain a quantile prediction result; based on the quantile prediction result, estimating a continuous probability distribution curve of a load common factor by adopting a non-parametric kernel density technology to obtain an interval prediction result; and constructing a multiple linear regression model to predict the load scale change, and adjusting the interval prediction result. According to the invention, the precision and calculation efficiency of load prediction are effectively improved.

The embodiment of the invention discloses a Crohn disease longitudinal queue course prediction system based on time perception Transform, and relates to the technical field of medical data processing, the system comprises a data preprocessing module, an input embedding layer, a Transform encoder and a multi-task multi-time window prediction header, the system breaks through the limitation that only baseline data is used for prediction of a traditional model, and the prediction efficiency is improved. According to the method, the full-cycle longitudinal data (such as inflammation indexes and medication records of each treatment) of the patient from the morbidity to the prediction can be integrated, and the disease progress, operation and medication risks of 1 year / 3 years / 5 years can be dynamically output. And reference is provided for clinicians.

The present application belongs to the technical field of data management, and particularly relates to a data management system for a twisted wire automatic detection platform, to solve the technical problem that the prior art cannot identify periodic defects and the traceability analysis process cannot correlate defects. The management system comprises: a data acquisition and synchronization unit for forming a longitudinal data profile; a main data modeling unit for constructing a pedigree main data model containing twisted wire product, single wire raw material and production process data; a data warehouse hierarchical processing unit for establishing a hierarchical data warehouse; an identification unit for constructing a data theme layer above the data detail layer; and an application service unit for constructing a data application layer above the data theme layer. The present application realizes traceability from quality problems to production, and provides data decision basis for process improvement and quality control.

The invention provides an abnormal data processing method, system and device for a wind turbine generator, and relates to the technical field of data processing, and the method comprises the steps: carrying out the window time sequence monitoring of Q wind turbine generators of a target wind power plant at a first monitoring window according to a preset monitoring index; performing longitudinal data expansion trend time sequence iteration on Q window time sequence monitoring data sequences; in combination with a density propagation mechanism, performing transverse consistency analysis on the Q longitudinal data expansion trend characteristics; and performing memory independent verification on the M abnormal longitudinal data expansion trend characteristics, and performing identification early warning on a corresponding window time sequence monitoring data sequence in the Q window time sequence monitoring data sequences according to the identified N verified abnormal longitudinal data expansion trend characteristics. Through the method and the device, the technical problem of relatively low abnormal data processing accuracy of the wind turbine generator in the prior art can be solved, and the technical effect of improving the abnormal data processing accuracy is achieved.

The utility model belongs to the field of highway construction, specifically relates to a highway engineering original road flat, longitudinal data quick extraction auxiliary device, and the device includes: base, track, measuring device, supporting device, quick -detachable connecting portion and automatic correction device, the side portion mounting of base track, supporting device swing joint is in base, automatic correction device installs in the lower part of base, the bottom of measuring device is provided with the mounting rod, the hole is seted up on base, the lower extreme of mounting rod passes through the hole on base and is connected with automatic correction device, one end of quick -detachable connecting portion is connected with mounting rod, and the other end is connected with supporting device, and the electronic level gauge is installed on measuring device. The device can avoid the influence of inertia or uneven road to equipment when running, can be automatic, keep the accuracy of measurement data.

The invention discloses a power equipment state evaluation method based on a normal cloud model and gray fixed weight clustering, and belongs to the field of data processing. Comprising the steps of determining a basic parameter system suitable for state evaluation of the whole and basic units of the power equipment, and constructing a state evaluation key parameter system; checking data quality in the key parameter system, and performing data cleaning work if necessary; establishing a data analysis model, judging the abnormal change of the equipment state and the association relationship thereof by utilizing the correlation between the longitudinal data and the transverse data, and performing equipment anomaly detection by analyzing the association relationship and the change rule of the multi-dimensional state data; characteristic parameters related to the key performance and the coupling relation between the characteristic parameters and the key performance state of the equipment are determined, the abnormal key performance is screened out, and the evaluation value of each key performance is obtained. According to the method, a normal cloud model is used for effectively fusing and cleaning state monitoring data of the power equipment, and a grey weighting clustering method is used for deeply analyzing the fused data, so that the state of the power equipment is accurately evaluated.

The invention relates to a method (200, 300) for characterizing a structure (104) etched in a first surface (140) of a substrate (102), such as a wafer, the structure (104) extending into the substrate (102) in a longitudinal direction z, the method (200, 300) being implemented by a characterization system (100) comprising a light source (130, 131) configured to emit an illumination beam having a wavelength suitable for transmission through the substrate (102), the invention relates to a method (200, 300) for imaging a substrate (102), comprising a first surface (140) of the substrate (102), and an imaging device positioned facing a second surface (147) of the substrate (102) opposite the first surface (140), the method (200, 300) comprising the steps of:-illuminating (202) at least one structure (104) with an illumination beam,-subsequently positioning (204) an object plane of the imaging device at at least two different longitudinal positions; -acquiring (206) at each of said longitudinal positions at least one image of the structure (104), said image being acquired through the substrate (102); -measuring (210) at least one transverse data relating to a transverse dimension of the structure (104) from at least one image acquired at each of the longitudinal positions; and determining (212) at least one piece of longitudinal data relating to the longitudinal shape of the structure from the transverse data of the at least two longitudinal positions. The invention also relates to a system for implementing such a method.

The application relates to the field of laser marking, and discloses a laser tab cutting control method, a device, a control terminal and a storage medium, the method comprising the following steps: segmenting tab cutting data to obtain longitudinal data segments and transverse data segments; determining the type of the data segment to be cut at the current moment, if the longitudinal data segment, the coordinate data of the longitudinal data segment is sent to a first processor, if the transverse data segment, the coordinate data of the transverse data segment is sent to a second processor; after the first processor receives the longitudinal data segment, the offset coordinates of the longitudinal line segment are calculated according to the conveying speed of a conveying belt, and the offset coordinates are sent to the second processor, and the second processor controls the movement of a galvanometer according to the offset coordinates; after the second processor receives the transverse data segment, the marking distance of the galvanometer is calculated according to the conveying speed of the conveying belt, and the movement of the galvanometer is controlled according to the marking distance. The calculation of the tab cutting is separated, the calculation pressure is reduced, and the reaction speed is accelerated.

Systems and methods for managing progression of a clinical trial. Input data for a machine learning model is formed, based on longitudinal data for clinical trial cohort. The input data corresponds to input features and the cohort includes a plurality of subjects. A clinical outcome output is generated for each subject, using the machine learning model and a portion of the input data corresponding to each subject. Feature importance values are generated, based on the machine learning model generating the clinical outcome output for each subject. The feature importance values include, for each subject, a set of feature importance values for a set of input features. A ratio of interest is computed using the plurality of feature importance values. An output is generated using the ratio of interest in which the output indicates whether the cohort should proceed to a next phase of the clinical trial.

The utility model provides a hand-held SLAM device which comprises a handle, a main frame, a laser radar, a motor and a synchronizing wheel, a laser radar support is arranged on the inner side of a main body of the laser radar, and the laser radar support is fixedly connected with a rotating shaft of the synchronizing wheel driven by the motor. Therefore, when the laser radar performs circumferential scanning in the circumferential direction and the pitch angle range of the preset range, the synchronous wheel is driven by the motor to drive the laser radar to perform transverse overturning around the horizontal axis of the laser radar, so that the radar can collect longitudinal data through transverse overturning while performing circumferential scanning. The vertical field of view is expanded for the laser radar, more environmental data can be acquired, and the sensing accuracy of the laser radar to a complex environment is improved.

The power transmission channel data processing method based on artificial intelligence provided by the application comprises the following steps: acquiring a standard image of a patrol area of a power transmission channel and setting an identification anchor point; receiving original images transmitted by different patrol devices, embedding a detection anchor point, identifying and matching the original images with an image set of the same scene; performing data alignment processing on the original images in the image set according to the position of the identification anchor point in the standard image by using a feature alignment algorithm to obtain aligned images; extracting feature fingerprints of the detection anchor points in the aligned images of the same scene of different patrol devices, comparing the feature fingerprints two by two to obtain comparison results; eliminating the aligned images that do not meet the requirements based on the comparison results to form a cross-device patrol image set of the same patrol area; and performing longitudinal data processing and horizontal data comparison based on the identification anchor point and the detection anchor point to obtain the cross-device patrol image set, which can improve the accuracy of subsequent defect identification and ensure the stable operation of the power system.

The invention discloses a method and a system for constructing a chronic lung disease re-admission risk assessment model, and relates to the technical field of medical information processing, and the method comprises the following steps: obtaining a clinical data record of a target patient; extracting a historical longitudinal data set; converting the clinical parameter fields into feature vectors; calculating a static risk factor and generating a dynamic risk factor through a static risk factor calculation module and a dynamic risk factor generation module based on a rule and a weight preset by prior medical knowledge; constructing a multi-dimensional risk feature space and standardizing each dimension; carrying out weighted transformation on the feature space by adopting a predefined architecture and a nonlinear model with fixed parameters, and outputting a quantitative re-admission risk score; and the score, the factor contribution and the time trend are visually presented through an interactive interface. According to the method, data training is not needed, logic is transparent, individualized and dynamic risk assessment is supported, and clinical interpretability and practicability are improved.

This invention relates to the fields of biomedical informatics and machine learning, specifically to a method for generating multimodal longitudinal data for Alzheimer's disease. This method constructs a brain network graph based on structural connectivity, using brain regions as nodes and connection strengths as edges to form a graph topology for modeling pathological spread. A continuous-time dynamic system parameterized by Neural ODE is defined on this graph, dynamically decomposing the disease course into three interpretable modules: generation and degradation, biomarker interactions, and brain network spread. The model initializes the pathological state from a conditional generator, obtains continuous trajectories through ODE numerical solutions, and jointly evaluates the realism and graph physical consistency of the generated data using a discriminator. This method can generate multimodal longitudinal data that conforms to the temporal and spatial spread patterns of AD, and is suitable for disease simulation, data augmentation, and clinical research.

Systems and methods are disclosed for identifying relationships between complex datasets and / or high-dimensional datasets for predictive modeling. The method includes clustering data associated with one or more entities in a first dataset based on distance data; clustering the data associated with the one or more entities in a second dataset based on longitudinal data; consolidating the first dataset and the second dataset into a third dataset based on weights assigned to one or more edges between one or more nodes in the first dataset and the second dataset; generating a diagnosis space indicating a condition of the one or more entities based on the third dataset; and determining, via input of the diagnosis space into a machine learning model, an optimization of a weighting scheme for assigning weights to one or more features within the longitudinal data.

The invention belongs to the technical field of pension service, and relates to an individual and environment matching system and method for governing social distraction of old people, and the system comprises a data collection and integration module, a matching consistency evaluation module, a matching mode recommendation module and a process feedback regulation and control module. Multi-dimensional longitudinal data are integrated through a data merging analysis technology, a UTADIS multi-criterion classification method is adopted to accurately identify the matching degree and treat dilemma, a fuzzy multi-attribute group decision and a TOPSIS method are combined to recommend a personalized matching mode, and a mixed time-varying effect model and an SOC measurement scheme are utilized to realize dynamic feedback regulation and control; the problems that an existing old-age nursing matching system cannot adapt to the social distraction treatment scene, the evaluation precision is low, and dynamic adjustment lags behind are solved, the pertinence and effectiveness of social distraction treatment are improved, and the method is suitable for the social distraction intervention scene of the old people in the old-age nursing institution.

In some embodiments, the present disclosure provides for an exemplary computer-implemented system that may include a longitudinal data engine, including: a processor and specialized index generation software to generate: an index data structure for a respective event type associated with each respective subject or object; where each respective index data structure is a respective event type-specific data schema, defining how to store events of a particular event type to form longitudinal data of each respective subject or object; an ontology data structure that is configured to describe one or more properties of a respective event of a respective subject or object; and longitudinal data extraction software to extract a respective longitudinal data for a plurality of index data structures and a plurality of ontology data structures associated with a plurality of subjects or objects.

A patient portal system may include a patient database module to interpret a patient profile value and a clinical database module to interpret a clinical study description. The patient database module may also interpret patient clinical data in response to the patient profile value and the clinical study description, and include at least a portion of the patient clinical data as longitudinal data of a patient corresponding to the patient profile value. The system may also include a patient interface module to implement a patient engagement interface and to interpret a patient medical event value in response to patient interactions with the patient engagement interface. The patient database may also adjust the longitudinal data in response to the patient medical event value.

The present disclosure relates to the technical field of electric power engineering, in particular to a two-stream Long Short-Term Memory (LSTM) method for predicting power load of port shore power. Loads. The method entails collecting longitudinal data to identify factors that affect power load data, performing correlation analysis to classify dominant and auxiliary features power loads; separately modeling the dominant and auxiliary features and generating a fusion feature map; constructing a Bayesian Optimization-Long Short-Term Memory (BO-LSTM) neural network, and inputting a fusion feature map into a two-stream time series learning module, extracting a deep representation of the dominant and auxiliary features, then introducing a channel attention mechanism is to weight a fusion feature vector, and outputting a power load prediction value by a residual correction module. The present disclosure significantly improves the prediction accuracy and robustness, and supports the real-time scheduling of the port shore power system.

The invention belongs to the technical field of medium station management, and particularly relates to a data medium station management system for a stranded wire automatic detection platform, so as to solve the technical problems that periodically appearing defects are difficult to identify and the defects are difficult to associate in a tracing analysis process in the prior art. The management system comprises a data acquisition and synchronization unit used for forming a longitudinal data profile; the main data modeling unit is used for constructing a pedigree main data model containing stranded wire products, single wire raw materials and production process data; the data warehouse hierarchical processing unit is used for establishing a hierarchical data warehouse; the identification unit is used for constructing a data theme layer on the data detail layer; and the application service unit is used for constructing a data application layer on the data theme layer. According to the invention, tracing from quality problems to production is realized, and a data decision basis is provided for process improvement and quality control.

The invention discloses a new energy station equipment state online checking system based on a longitudinal data chain, and belongs to the technical field of new energy stations. According to the method, data acquisition is performed on equipment in a new energy station to obtain corresponding equipment data, data precision demand analysis is performed after data preprocessing to determine whether a data precision retention measure is taken or not, and then the collected equipment data is input into a digital twin model to form a longitudinal data chain; and finally, completing consistency check and integrity check of cross-level and multi-source data based on the longitudinal data chain, outputting an equipment state through the state evaluation model, and constructing an equipment state feature vector through the checked longitudinal data chain to obtain the equipment state and output a corresponding check result. The online checking precision of the new energy station equipment is ensured, and the problem that the checking precision is reduced due to the fact that high-frequency data needed by checking are filtered in the longitudinal link in the prior art is solved.