143 results about "Model system" patented technology

Embodiments described herein provide for a healthcare ecosystem of computing devices participating as nodes of a distributed ledger (e.g., blockchain). The distributed ledger includes NFTs representing corresponding patients, where the initial NET behaves logically as a token scaffold that represents a “digital twin” of the particular patient. The initial patient token represents personal profile data of patient personal data for the patient. The nodes may generate new data and / or new sub-tokens (or other blockchain entry type) representing new data, which the nodes associate with the token scaffold. The sub-tokens contain data for representing physical or digital specimens, health data, and data derived from the health data (e.g., cell lines, organoids, other model systems). As the nodes generate and associate new sub-tokens to the patient's token scaffold over time, the token scaffold graphs and facilitates integration of patient-specific data and evolves into a robust electronic health record (EHR) and digital twin.

The invention provides a cement production management and control platform PHM model system, and the system comprises the steps: obtaining the monitoring data and production management data of a cement production process, and enabling the monitoring data and production management data to form an original data set; performing data cleaning and preprocessing on the original data set, and removing noise and abnormal values to obtain a standardized data set; inputting the feature component into a preset deep learning model, and outputting a prediction result of the equipment state; the prediction result is compared with a set threshold range, and if the prediction result exceeds the threshold range, early warning information is generated; classifying the early warning information in combination with historical fault data and current production environment parameters, and distinguishing normal production fluctuation and potential faults; if the classification result is a potential fault, a fault mode is analyzed, a fault type is determined, a pre-training model is finely adjusted by using a fault sample, and the generalization ability of the model is improved; new data are continuously collected in the production process, the model is periodically updated, and the accuracy of model fault early warning is improved.

The invention discloses a multi-drive process multi-factor agricultural non-point source pollution prediction method based on coupling meteorological numerical forecasting. The method comprises the following steps: firstly, introducing numerical weather forecast data, and constructing a high-precision weather driving field with kilometer-level space and hour-level time resolution by combining WRF dynamic downscaling, DEM terrain correction and conservation resampling; secondly, establishing a multi-drive process coupling model system which comprises a meteorological drive layer, a hydrological response layer, a pollutant migration layer and a crop feedback layer and is used for simulating runoff production, sediment erosion, nitrogen and phosphorus migration and transformation and crop transpiration and root nutrient absorption processes; thirdly, performing precision verification on a simulation result by using observation data, and identifying key meteorological and hydrological factors through an error transfer matrix and a sensitivity analysis method; and finally, realizing parameter adaptive correction by adopting a long short-term memory network, finishing parameter optimization in combination with a multi-target genetic algorithm, packaging the model chain through a containerization technology, and realizing cross-platform deployment and visual output of a pollution load result. The method can be used for agricultural non-point source pollution prediction and management.

The invention discloses a SysMLv2.0 model library multiplexing method based on definition driving. The method comprises the following steps: S1, constructing a model definition library containing structures, behaviors, interfaces, constraints and semantic attributes; s2, analyzing a modeling demand, identifying a to-be-multiplexed module, and determining a definition unit identifier; s3, calling the target definition unit and inserting the target definition unit into the current model in a reference mode; s4, performing context adaptation to generate a binding relationship; s5, setting a version identification mechanism, and establishing a version mapping index between the use unit and the definition unit; s6, registering a reference relationship and an adaptation rule, and generating a modeling metadata document; s7, automatically updating the content of the use unit according to the metadata document in the modeling process; and S8, executing consistency verification after use unit integration is completed, and ensuring correct interface connection, conflict-free constraint logic and complete version binding. According to the method, a definition-driven model system is constructed, and cross-context semantic multiplexing and version consistency modeling management are realized.

The invention belongs to the technical field of benign and malignant diagnosis model systems for solid pulmonary nodules, and particularly relates to a benign and malignant diagnosis deep learning model system for solid pulmonary nodules. Comprising an overall architecture system of the model, a data set introduction system, a data set preprocessing system, a lung CT image segmentation system, a lung nodule detection system, a lung nodule category system, an experiment parameter setting system, an evaluation index system and an experiment setting and result system. A lower half branch of the network is mainly designed, the lower half branch network extracts multi-scale feature information of a lung CT image through an Inception module, and convolutional attention modules such as ECA and CBAM are introduced to enhance the extraction effect of the lower half branch network on position and shape features of the lung CT image.

A system for online-coupled atmospheric chemical transport modeling and data assimilation is built using online coupling and secondary parallelization. In the prototype code of a nested air quality forecasting model system, a parallel data assimilation framework routine is introduced. The system includes observation modules, model modules, and assimilation modules. The observation module is responsible for flexible access and preprocessing of various component-type observation data. The model module handles the model integration of initial fields, involving calculations of physical and chemical processes. The assimilation module performs analysis assimilation of model state variables. This system meets the requirements for coordinated assimilation of multiple chemical component variables, simultaneous introduction and flexible combination of various types of observation data, and effective handling of non-linear and non-Gaussian distribution issues in chemical assimilation.

The invention provides an otological disease auxiliary diagnosis large model construction method and a large model system, and relates to the technical field of multi-modal learning modeling, and the method comprises the following steps: building four diagnosis sub-models, comprising a hearing loss evaluation model, an auditory meatus and tympanic membrane / middle ear state or lesion evaluation model, an auditory meatus and middle ear structure evaluation model and a medical history and medical record model. The four diagnosis sub-models are trained; the outputs of the four diagnosis sub-models are fused, and a large ear disease clinical diagnosis model is constructed; evaluating and optimizing the performance of the otological disease clinical diagnosis large model; according to the method, multiple sub diagnosis models are fused, multi-source heterogeneous data are used for training, the generalization ability of the model can be improved, the model can adapt to different application scenes, the decision process of the model can be better understood by analyzing the output of each sub diagnosis model, and the credibility of the model is improved.

The invention provides a method and a device for configuring a motion intention decoding model based on electroencephalogram signal data, which are used for providing a set of novel training mechanism of the motion intention decoding model, and performing conjoint analysis on the electroencephalogram signal data and motion posture data obtained by analyzing videos in the training process. A model system can quickly capture association between neural signals and motion tracks and comprehensively capture motion intentions, the recognition precision of the model for various complex motion modes is remarkably improved, meanwhile, computing resources needed by model processing are reduced, the whole decoding process is more efficient, individual adaptability and generalization are considered, and the method is suitable for popularization and application. And the real-time application requirement of intrusive brain-computer interface research can be well met.

The invention relates to the technical field of disease course trend modeling, in particular to a subarachnoid hemorrhage disease course trend modeling system fusing multi-source data. Four kinds of signals of intracranial pressure, blood flow velocity, cerebrospinal fluid pressure and electroencephalogram of a monitored object are synchronously collected, an instantaneous phase is extracted through Hilbert transform, a time window is adaptively adjusted according to the brain blood vessel conduction delay characteristic of an individual, and phase locking indexes among three pairs of signals are calculated. A multivariate coupled oscillator model is established, phase track topology invariant features are extracted, and comprehensive trend indexes are generated through tensor fusion. An individualized four-dimensional phase entropy baseline mode is established, and a double-layer early warning mechanism is adopted: when second derivative continuous symbol overturning occurs in all three phase locking indexes, early warning is directly performed, and when any two phase locking indexes are overturned, a trend index needs to be synthesized for confirmation. And predicting a state level, a trend level and an expected evolution trajectory based on a support vector regression model. According to the invention, precise disease course prediction and early warning are realized, and a basis is provided for clinical decision making.

The application provides a configuration method and device of a motor intention decoding model based on electroencephalogram signal data, and provides a novel training mechanism of the motor intention decoding model. In the training process, the electroencephalogram signal data and the motor posture data obtained by analyzing the video are jointly analyzed, so that the model system can quickly capture the correlation between the neural signals and the movement trajectory, comprehensively capture the motor intention, significantly improve the recognition accuracy of the model for various complex movement modes, reduce the required computing resources of the model processing, make the entire decoding process more efficient, and balance the individual adaptability and the generalization, so that the real-time application requirements of the invasive brain-computer interface research can be well met.

Methods and systems for foveal avascular zone (FAZ) segmentation of a retina. A three dimensional optical coherence tomography angiography (OCTA) volume of a retina of a subject is received. The OCTA volume comprising a plurality of layers. A slab input for a model system is formed using the OCTA volume. The model system comprising a deep learning model. A set of mask images is generated, via the model system, based on the slab input. The set of mask images includes an area mask image that accurately and reliably identifies an area of a foveal avascular zone captured by the OCT volume.

Modular mucosal tissue bioreactors combined with bioartificial mucus provide a high-fidelity, medium-throughput platform for evaluating CF treatments and modeling host-pathogen biology, a stepping stone towards better options for HEMT-nonresponsive patients. Whole lung ex vivo Cystic Fibrosis model system allows for investigation of candidate CF therapies.

The invention discloses a chronic obstructive pulmonary disease high risk dynamic assessment model system based on an artificial intelligence algorithm, relates to the technical field of chronic obstructive pulmonary disease high risk assessment, and aims to solve the technical problem that the timeliness and accuracy of an assessment result of an existing assessment model are limited. The medical data acquisition module is used for acquiring medical data of a subject through a wearable device, an electronic medical record system or a manually input multi-source channel, generating standardized preprocessed medical data after performing format cleaning, missing value filling and abnormal value filtering on the data, and transmitting the standardized preprocessed medical data to the memory of the cloud database; and the artificial intelligence dynamic assessment model construction module is used for constructing an artificial intelligence assessment model fusing time sequence characteristics and a nonlinear relationship based on the preprocessed medical data, and generating an intermediate result and a final parameter of risk assessment through multi-level calculation. The method has the advantage that the high risk of chronic obstructive pulmonary disease can be dynamically evaluated in real time.

The present disclosure provides, inter alia, methods for treating hemophilia A as well as computer-based-systems, including software-based pharmacokinetic model systems, and their use to determine dosing information for subjects who have hemophilia A.

The invention discloses a knee arthritis image measurement model system and method based on deep learning, particularly relates to the technical field of image processing, and is used for solving the problem of low image measurement accuracy. According to the method, the image preprocessing technology and the deep learning segmentation method are integrated, high-precision automatic segmentation and analysis are performed on the knee joint image, the contrast ratio and the resolution ratio of the image are enhanced, meanwhile, the image quality is further improved through biased field correction and non-local mean value noise reduction, and the image segmentation is performed by adopting the U-Net model, so that the image quality is improved. A complex structure in the joint is effectively recognized, segmentation precision is improved, a segmentation strategy is dynamically adjusted according to measurement influence information generated in the segmentation process, the processing flow is optimized in real time through different regulation and control signals, diagnosis efficiency and accuracy are improved, and therefore the automation level and reliability of medical image processing are improved, and the medical image processing efficiency is improved. Efficient and accurate support is provided for clinic, and the accuracy of osteoarthritis image measurement is improved.

The embodiment of the invention discloses a causal inference method for traffic accident data processing and prediction model optimization, and the method comprises the steps: constructing a multi-source heterogeneous accident database, and constructing a standardized feature matrix; based on the standardized feature matrix, extracting a feature constraint rule through a decision tree, and generating a minority class anti-fact sample conforming to domain knowledge in combination with a heuristic algorithm to form an equalized data set; inputting the equalized data set into a double-path model system for training, expanding a feature space by a data path to improve the characterization capability of a minority class, and embedding an interpretability constraint module into an interpretation path to obtain a trained model; based on the trained model, constructing a visual interpretation system comprising a feature importance map and a causal association network; and based on a visual interpretation system and the standardized feature matrix, constructing a multi-level causal network identification core influence factor, and quantifying the causal effect of intervention measures through anti-fact simulation to form a priority intervention list.

The invention relates to the technical field of three-dimensional modeling, and discloses a modeling system fusing a laser point cloud and a two-dimensional vector, and the system comprises a data collection module, a data fusion module, a model generation module, a visualization module, an optimization adjustment module, and a user feedback module. Laser point cloud and two-dimensional vector data are acquired through a data acquisition module and standardized preprocessing is carried out; performing registration, conversion and weighted fusion on the point cloud and vector data based on a data fusion module to generate high-precision fusion data; performing three-dimensional reconstruction, detail enhancement and quality verification through a model generation module, and outputting a final model; utilizing a visualization module to render a scene and support interactive operation; evaluating model efficiency and dynamically optimizing parameters through an optimization adjustment module; and a user feedback module is combined to analyze requirements and adaptively adjust system settings. According to the method, the precision and reliability of three-dimensional modeling are improved, the robustness and consistency of the modeling process are enhanced, and the overall output quality and engineering applicability of the system are improved.

The invention relates to the technical field of tree species identification, in particular to a tree species identification method and system based on multi-source remote sensing data fusion, and the method comprises the steps: obtaining multi-source remote sensing data in a target identification region, carrying out the cross-domain feature decoupling processing of the multi-source remote sensing data, and obtaining the attribute features of tree species and the environmental interference features; inputting the tree species attribute features into a preset adaptive spatial-temporal feature library to obtain correction features; collecting current real-time environment parameters, and fusing the tree species attribute features, the environment interference features, the correction features and the real-time environment parameters to obtain multi-source coordination features; a tree species identification result is generated according to the multi-source coordination characteristics, tree species identification requirements in different geographical environments can be adaptively matched, the problem of precision attenuation caused by data distribution offset during cross-regional migration of a model system is effectively solved, and the tree species identification robustness in different ecological environments is remarkably improved.

An oncological foundation model is trained with broad, multimodal data to make predictions concerning a variety of different types of cancers. For example, the foundation model may make use of medical images drawn from radiology and pathology, as well as immunohistochemistry data; the presence or absence of biomarkers for particular diagnoses; patient history data; patient demographic data; and other forms of medical data. When using medical images, whole medical images as well as feature sets derived from the medical images may be used. The foundation model may have both causal predictive abilities as well as generative abilities.

The invention belongs to the technical field of medical large models, and relates to a lightweight medical large model system driven by a knowledge graph. Comprising a questioning large model for collecting patient information through multiple rounds of dialogues, a summarizing model for comprehensively analyzing symptoms and medical history of a patient from the patient information and providing a preliminary diagnosis result and treatment suggestions, and a symptom entity and a disease entity from the patient information and the diagnosis result. Constructing a first candidate disease entity list by using the extracted disease entities, and constructing a second candidate disease entity list by acquiring related disease entities from a knowledge graph according to the extracted symptom entities, according to all the candidate disease entities, obtaining an extraction verification summary model of supplementary knowledge information of all the candidate disease entities from the knowledge graph; according to the method, the diagnosis process is perfected, the illusion problem is relieved, the interpretability is enhanced, and quick response and low-cost iteration are realized.

Systems and methods include techniques associated with one or more machine learning systems to predict peptide binding interactions for a relevant target. The one or more machine learning systems may be used to generate a ranked set of results that can be tested and then used to retrain the one or more machine learning systems as part of a validation process. The trained model may then be deployed to predict peptides for a given target condition.

Systems and techniques are described herein for executing a machine learning model. For instance, a process can include: receiving input data; processing the input data using a first machine learning (ML) block to generate intermediate data, the first ML block including a first set of parameters; processing the intermediate data using a second ML block to generate processed data, the second ML block including a second set of parameters matching the first set of parameters; and outputting the processed data.

The invention relates to the technical field of strategy evaluation simulation systems, in particular to an urban sustainable development evaluation simulation system and method, and the system comprises a strategy module, an industrial module, a social economy module, an energy module, a water resource module, an agriculture module, an ecological environment module, a land module and a region welfare climate scene module. The module is used for predicting changes of different social ecosystem elements in the future and providing quantitative support for sustainable development. According to the invention, a meta-model system is established, integration of multiple modules and multiple methods is realized, and a complete and advanced evaluation system and integration framework are formed. According to the invention, the elements of each module and the mutual relation among the modules can comprehensively consider multiple aspects of urban development, and comprehensively consider the welfare levels of three dimensions of economy, society and environment, thereby reflecting the comprehensiveness of sustainable development, and improving the development efficiency through the positive cycle of welfare-investment-growth. And interaction and mutual promotion among elements of sustainable development are emphasized, and the accuracy and reliability of evaluation are improved.

Systems and methods include techniques associated with one or more machine learning systems to normalize disparate entries within one or more datasets for common data types. The one or more machine learning systems may be used to generated relationships between attribute-value pairs associated with a particular data type and then to determine, from a corpus of free-form data, individual entries for a target data type. The identified individual entries may be used to extract information from the dataset and generate a modified, clean dataset.

The application discloses a geothermal caprock evaluation modeling method and belongs to the technical field of geothermal resource exploration. In view of the problems that existing caprock evaluation standards are uneven, subjective and lack of quantitative coupling of structural factors and calibration of measured data, the application firstly acquires caprock physical properties and structural data; secondly, a single-factor evaluation model based on physical mechanism is constructed for lithology, thickness, permeability and thermal conductivity; on this basis, a structure-mechanical-integrity coupling model is constructed, which includes basic structure scores, fracture risk indexes and plastic self-repairing capacity factors; finally, by using observation data such as historical breakthrough pressure and leakage events, key parameters in the above model are calibrated through regression and Bayesian methods. The method forms a unified and portable caprock evaluation basic model system, reduces subjectivity, and improves the stability and interpretability of the evaluation results.

Systems and methods for learning from mistakes to improve detection rates of Machine Learning (ML) models. The systems and methods including receiving data with labels; running the data through a trained ML model for predictions; identifying errors in the predictions based on the labels received with the data; adjusting weights associated with samples in the data based on the identified errors; and retraining the ML model with the adjusted weights.

The application relates to a calculation method of a high-temperature alloy impurity diffusion coefficient and an atomic mobility parameter based on a molecular dynamics simulation, which comprises the following steps: parameter setting; model establishment; setting a potential function as a multicomponent EAM alloy potential; energy minimization and setting an initial atomic speed; calculating RDF values of a model system and drawing RDF curves; calculating MSD values of all atoms and respectively outputting MSD values of various atomic groups according to various different metal atom types; defining thermodynamic physical quantity output and commanding each step to output and save; giving a random initial speed to the system, and running under each system in three stages: a temperature rising stage, a temperature keeping stage and continuous temperature keeping and relaxation, and outputting MSD values; calculating the impurity diffusion coefficient of the system based on the MDS values; and taking the measured impurity diffusion coefficient as an input value, and obtaining the atomic mobility parameter through DICTRA software. Compared with the prior art, the application has the advantages of simple method, accurate numerical value and the like.

The invention discloses a breeding performance prediction system based on an intelligent sheep breeding platform, and belongs to the technical field of animal husbandry informatization. The intelligent breeding platform is designed, standardized collection, storage and intelligent management of sheep full-life-cycle data are achieved, and a solid data foundation is laid for follow-up research; secondly, innovatively combining ensemble learning (AdaBoost, GBRT and the like), machine learning (SVR, KNN and the like) and a sorting learning algorithm, and constructing a multi-level breeding sheep breeding performance prediction model system; particularly, a subjective and objective combination weighting method based on AHP-PCA is provided, and the prediction precision of the model is remarkably improved through feature reconstruction; in addition, an Achimedes optimization algorithm (AOA) is introduced into the field of breeding prediction for the first time, and adaptive search of hyper-parameters is realized by using a physical simulation mechanism of the AOA, so that the accuracy, efficiency and robustness of the model are remarkably improved.

Systems and methods for use in identifying weights to be employed in a selection algorithm associated with plant advancement are disclosed. One example method includes identifying a start set of weights for a selection algorithm associated with a breeding program, and for each scale parameter value in a schedule, and for each of N iterations, modifying the start set of weights based on the scale parameter value, identifying a set of germplasm based on at least the modified set of weights, advancing the modified set of weights to a next iteration as the start set of weights when certain criteria are satisfied, and identifying the modified set of weights as an output when the iteration is equal to N.

Systems and methods for generating a large volume of synthetic stream-of-commerce security imaging data is disclosed. Methods for creating synthetic baggage x-ray scans, synthetic passenger millimeter wave scans, synthetic passenger video surveillance data, and introducing prohibited items to real security images are also disclosed.