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61 results about "Causal model" patented technology
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In philosophy of science, a causal model (or structural causal model) is a conceptual model that describes the causal mechanisms of a system. Causal models can improve study designs by providing clear rules for deciding which independent variables need to be included/controlled for.
Systems and methods for modeling and analyzing networks
The systems and methods described herein utilize a probabilistic modeling framework for reverse engineering an ensemble of causal models, from data and then forward simulating the ensemble of models to analyze and predict the behavior of the network. In certain embodiments, the systems and methods described herein include data-driven techniques for developing causal models for biological networks. Causal network models include computational representations of the causal relationships between independent variables such as a compound of interest and dependent variables such as measured DNA alterations, changes in mRNA, protein, and metabolites to phenotypic readouts of efficacy and toxicity.
Owner:GENE NETWORK SCI
System and method for the automated presentation of system data to, and interaction with, a computer maintained database
InactiveUS20030061072A1Improve efficiencyEasy to take care ofComputer-assisted medical data acquisitionDiagnostic recording/measuringDiseaseDiscussion group
A system and method are provided for extracting a set of data from a system user descriptive of the complete health snapshot of the user's to interact with a database of numerous other users so as to generate a cluster of similar user's exhibiting a similar (within some system defined distance metric) health snapshot. The system guides the user to present his or her data via a complex questionnaire based upon a novel descriptive taxonomy, based upon the principles of "cyberhealth" as opposed to the standard medical "disease oriented" singular cause and effect model. The system generates the cluster of similar users, analyzes the cluster to obtain a ranked list of possible remedies or therapies to assist the user in dealing with health problems. The system further creates a computer networked virtual community of users with common health problems / interests, facilitates online chat, discussion groups, and the trading of health information. Additionally, the system provides listings of and links to health care providers and medical testing laboratories who are able to assist users of the system.
Owner:MEDIGENESIS
Causal modeling of multi-dimensional hierachical metric cubes
A computing system initializes a first frontier to be a root of a multi-dimensional hierarchical data structure representing an entity. The system acquires first data corresponding to the first frontier. The system performs modeling on the first data to obtain a first model and a corresponding first statistic. The system expands a dimension of the first frontier. The system gathers second data corresponding to the expanded frontier. The system applies the data modeling on the second data to obtain a second model and a corresponding second statistic. The system compares the first statistic of the first model and the second statistic of the second model. The system sets the second model to be the first model in response to determining that the second model statistic is better than the first model statistic. The system outputs the first model.
Owner:IBM CORP
Method and system for causal modeling and outlier detection
Owner:GLOBALFOUNDRIES INC
Method For Controlling The Appearance Of Products And Process Performance By Image Analysis
InactiveUS20080013821A1Improve surface qualityImage enhancementImage analysisMachine visionImaging analysis
A new application of machine vision for process industries is proposed. The invention consists of: (1) estimation of visual quality of products, (2) modeling causal relationship between estimated quality and process variables, and (3) optimization of visual quality using the causal model. This invention can handle the stochastic nature in visual appearance of products that process industries provide, which has been a main obstacle for the success of machine vision in process industries. Also, it opens new tasks in machine vision such as modeling and optimization of visual quality of products.
Owner:MCMASTER UNIV
System and method for the automated presentation of system data to, and interaction with, a computer maintained database
InactiveUS7676384B2Computer-assisted medical data acquisitionDiagnostic recording/measuringDiseaseDiscussion group
Owner:MEDIGENESIS
System, method, and computer program product for combination of cognitive causal models with reasoning and text processing for knowledge driven decision support
ActiveUS20050197992A1Increase or decrease weightImprove relevanceDigital data information retrievalSemantic analysisDomain modelProcessing element
A system, method, and computer program product for combining causal domain models with reasoning and text processing for knowledge driven decision support are provided. A knowledge driven decision support system is capable of creating a domain model, extracting and processing quantities of text according to the domain model, and generating understanding of the content and implications of information sensitive to analysts. An interface may be used to receive input to model complex relationships of a domain, establish implications of interest or request a query, and update the causal model. A processing element can capture and process text into text profiles by incorporating the domain model and process the text profiles in accordance with the domain model by applying formal reasoning to the information to derive trends, predict events, or arrive at other query results. An output element can provide a user the resulting information related to the domain model.
Owner:THE BOEING CO
Method and system for causal modeling and outlier detection
InactiveUS20080270088A1Rich expressivenessEfficient processingSimulator controlDigital computer detailsData setAnomaly detection
Owner:GLOBALFOUNDRIES INC
System and method for creating a simulation model via crowdsourcing
InactiveUS20150339415A1Error and minimized and avoidedTime minimized and avoidedCAD network environmentDesign optimisation/simulationStable stateAlgorithm
The disclosed systems and methods transform descriptive causal models into digital computer simulation models based on information obtained from crowdsourcing. This may include interviewing experts to collect descriptive information that is used to assemble causal descriptive models, which can be represented as graphs of nodes connected by edges. Node values may represent concepts and edge weights represent their causal relationships. Crowdsourcing is used to collect feedback about the causal descriptive models. The feedback is used to calculate edge weights that are incorporated into causal simulation models for use during model processing runs. A digital computer simulation is completed when node values reach steady states after model processing runs. A computer visualization tool can then be used to analyze outcome spaces produced by digital computer simulations. For example, digital computer simulations can generate decision spaces that are used to determine preferable courses of action in different situations.
Owner:MITRE SPORTS INT LTD +1
Object-oriented nonlinear and non-causal modeling and simulation method for rotor dynamics system
InactiveCN102915388AConvenient analysis methodSpecial data processing applicationsModelicaMulti field
The invention relates to the technical field of simulated modeling of a rotor dynamics system and in particular to an object-oriented nonlinear and non-causal modeling and simulation method for the rotor dynamics system. The modeling and simulation method is based on an object-oriented non-causal multi-field modeling language Modelica; the modeling language Modelica is used for writing the simulated modeling program of the rotor dynamics system; the simulated modeling of the rotor dynamics system is formed by components and faults; the components comprises a turntable component, shaft section components and a bearing component; the turntable component, the shaft section components and the bearing component comprise two connector elements respectively; the faults comprise crack fault, rubbing fault and foundation loosening fault; and by the modeling and simulation method, the rotor dynamics systems with various structures can be modeled, system characteristics and working condition of the rotor dynamics systems can be truly reflected, the faults such as crack, rubbing and foundation loosening can be introduced to perform system analysis, and a convenient analysis measure is provided for engineering technical persons and scientific researchers.
Owner:SHANGHAI JIAO TONG UNIV
Dynamic human reliability analysis method and device for multi-unit nuclear power plant
InactiveCN109299868AIntegrity guaranteedGuaranteed accuracyCharacter and pattern recognitionNeural architecturesHuman error probabilitySystem dynamics
Owner:HUNAN INST OF TECH
Enterprise Evaluation Supporting Device
InactiveUS20080177592A1High adaptation and significanceImprove reliabilityFinanceForecastingStructure analysisIntellectual property
Input of cause-and-effect model information where the coefficients at which three evaluated values, i.e., the evaluated values of business strategy, research-and-development strategy, and intellectual property strategy influence the company evaluated value, the coefficients at which the three evaluated values and the company evaluated value influence the observable indexes respectively, the error variables with which the factors other than the three evaluated values and the company evaluated value give variations to the company evaluated value and the observable indexes respectively are assumed is received. From the inputted the cause-and-effect model information and the observable indexes, the estimates of the coefficients are calculated by covariance structure analysis. From the coefficients and the observable indexes, the three evaluated values and the company evaluated value are calculated. With this, the relationship among the three strategies which are directly nonobservable abstract factors, i.e., the business strategy, the research-and-development strategy, and intellectual property strategy and the company value is verified, and company evaluation based on these three strategies can be performed.
Owner:INTPROP BANK CORP (JP) +1
Change point detection in causal modeling
Owner:INT BUSINESS MASCH CORP
System, method, and computer program product for combination of cognitive causal models with reasoning and text processing for knowledge driven decision support
Owner:THE BOEING CO
Dyskinesia non-intrusive rehabilitative closed-loop brain-computer integrated system based on FPGA
InactiveCN105653873AAchieve outputReduce treatment riskMedical simulationHealth-index calculationClosed loopCortical neurons
The invention provides a dyskinesia non-intrusive rehabilitative closed-loop brain-computer integrated system based on an FPGA. The FPGA is used as a control core of the system, nuclei basales and thalamic-cortical prosthesis hardware model is set up, data obtained through calculation of a self-adaptation control algorithm based on the FPGA is used as input to control model parameter setting and force feedback and adjustment until an expected control result is achieved, the self-adaptation control algorithm based on the dynamic causal model is realized, force feedback signals are output, and therefore rehabilitation of patients with the dyskinesia nervous system diseases is achieved. Rehabilitation of the patients with dyskinesia nervous system diseases is achieved, and the complex nuclei basales and thalamic-cortical neuron network and the self-adaptation control algorithm of the dynamic causal model are modeled. The platform provides the effective theoretical basis and technical support for rehabilitation of the dyskinesia nervous system diseases and has important practical value in research on control and treatment on nerve diseases such as Parkinson's disease, epilepsia and alzheimer's disease.
Owner:TIANJIN UNIV
Systems and methods for modeling and analyzing networks
ActiveUS8571803B2Reduce in quantityDigital data processing detailsAnalogue computers for chemical processesMetaboliteNetwork model
Owner:GENE NETWORK SCI
Model-based dynamic analysis method for extruded two-component liquid power system
ActiveCN105975692AQuick buildReduce difficultySpecial data processing applicationsModelicaReusability
The invention discloses a model-based dynamic analysis method for an extruded two-component liquid power system. An object-oriented declarative liquid power system modeling method is formed by combining a liquid power system modeling simulation theory with a Modelica technical system and used for guiding a modeling process of a non-causal model library of the liquid power system. For a complex system such as the liquid power system, the system does not need to be decoupled, and component input / output and equation solving sequences do not need to be defined, so that the difficulty and complexity of system modeling are greatly lowered, the workload is reduced, an error caused by manually specifying the solving sequence is avoided, and the reusability, expandability, flexibility and knowledge accumulation capability of models are remarkably improved.
Owner:XIAN AEROSPACE PROPULSION INST
Determining performance in a distributed application or system
In one embodiment, the method includes determining one or more nodes associated with a treatment of a query; generating one or more stimuli associated with the treatment of the query wherein the or each stimulus are likely to perturb one or more resources within a system; measuring data at the or each node relating to the resources to determine the effect of the or each stimuli at the or each node; identifying one or more pairs of nodes which have a correlation in the measured data; transforming the correlation into a causal relationships where the cause is a measuring device measuring the response and the consequences are the other correlated measuring devices; generating a list of causal relationships; and combining different causal relationships into a causal model so that a chain of causal propagations can be built
Owner:ALCATEL LUCENT SAS
Judicial decision reasoning method based on interpretable causal model
InactiveCN111597307AHave objectivityNormativeData processing applicationsInference methodsCausal reasoningCorrelation function
The invention relates to the fields of machine learning, natural language processing, causal reasoning and the like, in particular to a judicial decision reasoning method based on an interpretable causal model. The judicial decision reasoning method comprises the steps of analyzing and representing judicial data, defining a judicial decision inference framework, and constructing a judicial decision inference cause and effect model. Wherein defining of the judicial decision inference framework comprises the steps of extracting fact elements, identifying the fact elements and obtaining a decision result. The model construction comprises the steps of obtaining a reason variable, obtaining a result variable, constructing a correlation function, and constructing a judicial decision reasoning cause and effect graph. According to the judicial decision reasoning method, causal reasoning of judicial judgment is realized, and the problems that an existing judicial judgment reasoning method lacksa reasoning mechanism and is poor in interpretability can be effectively solved, and the accuracy of a case judgment result easy to confuse can be obviously improved.
Owner:SHANXI UNIV
Method for controlling the appearance of products and process performance by image analysis
InactiveUS8131020B2Improve surface qualityImage enhancementImage analysisMachine visionImaging analysis
Owner:MCMASTER UNIV
Data-driven complex system mechanism automatic learning method, system and equipment
ActiveCN113177626ACapable of self-evolutionImprove description accuracyArtificial lifeMachine learningEngineeringComplex system
The invention belongs to the field of big data and machine learning, particularly relates to a data-driven complex system mechanism automatic learning method, a system and equipment, and aims to solve the problems that an existing system modeling technology is difficult to predict a behavior trend from field observation data, a reconstructed mechanism model is not matched with physical observation data, the robustness is poor and the like. The method comprises the steps of obtaining historical multi-modal data and real-time multi-modal data, constructing a time sequence long-range correlation hypergraph model through airspace circulation memory coding, performing normalized combination on the hypergraph model through a neural differential equation model, and performing automatic iterative search on a continuous game network structure to obtain a system mechanism continuous dynamic model, and performing biological evolution simulation to obtain a causal model, and recalculating an association weight to obtain an active early warning system. According to the method, non-linearity, emergence, balance step, adaptability and special property description of a feedback loop of a complex system are realized, and the prediction accuracy of the model is improved.
Owner:INST OF AUTOMATION CHINESE ACAD OF SCI
Systems and methods for predictive network modeling for computational systems, biology and drug target discovery
PendingUS20210005278A1Accurate predictionEnhanced Bayesian network learningBiostatisticsProbabilistic networksAlgorithmTheoretical computer science
Systems and methods for predictive network modeling are disclosed. The systems and methods disclosed compute a top-down causal model and a bottom-up predictive model and utilize those models to determine the conditional independence among multiple variables and causality among equivalent variable structures. Before or during modeling, the data is passed through Markov Chain Monte Carlo sampling.
Owner:THE ARIZONA BOARD OF REGENTS ON BEHALF OF THE UNIV OF ARIZONA +1
Brain link mining system based on document analysis and functional nuclear magnetic resonance imaging analysis
InactiveCN103678895AFast operationReduce in quantitySpecial data processing applicationsDocument analysisBiological activation
Owner:BEIJING INSTITUTE OF TECHNOLOGYGY
Method for generating big data causal model in corresponding power grid operation environment
The invention relates to a method for generating a big data causal model in a corresponding power grid operation environment, and belongs to the technical field of power grid automatic scheduling. Themethod comprises the following steps: 1) obtaining a correlation factor set of events occurring in the operation process of current power grid equipment; 2) screening out high-probability associationfactors of which the correlation with the event is greater than a preset value from the association factor set; 3) performing causal relationship detection on the high-probability association factorsand the events, and determining strong association factors; 4) generating a big data causal model by utilizing a Bayesian causal network and regulation big data based on the strong correlation factors; and 5) carrying out matching combination on the big data causal model and a preset experience model to generate a regulation and control business big data causal model of the current power grid equipment. By applying the method, the causal relationship between the defect event of the power grid equipment and each factor influencing the time can be intuitively reflected, so that an observer canquickly obtain the cause of the defect of the power grid equipment in an emergency situation.
Owner:JIANGSU ELECTRIC POWER CO +2
Systems and methods for generating a genotypic causal model of a disease state
A system for generating a genotypic causal model of a disease state includes a computing device that generates a causal graph containing genotypic causal nodes and connected symptomatic causal nodes, which contains causal paths from gene combinations to symptomatic datums. Genotypic causal nodes and / or connected symptomatic causal nodes may be generated by feature learning algorithms from training data.
Owner:KPN INNOVATIONS LLC
Competitive risk survival analysis method based on causal inference
PendingCN114418420ANot affected by spurious correlationsCharacter and pattern recognitionResourcesData miningCausal inference
The invention discloses a competitive risk survival analysis method based on causal inference. The method comprises the following steps: building a structured causal model according to a competitive risk survival analysis model; confusion factors existing in the competitive risk survival analysis model and back door paths generated by the confusion factors are identified according to the structured causal model; performing causal intervention on the competitive risk survival analysis model through backdoor adjustment to remove confusion factors in the model; defining a loss function of the competitive risk survival analysis model, and correcting the loss function to obtain a loss function after causal intervention; and minimizing a loss function after causal intervention to realize training optimization of the competitive risk survival analysis model. According to the competitive risk survival analysis method based on causal inference, an existing competitive risk survival analysis model is corrected from a causal angle by using a structured causal model, and a deviation-removed survival model is learned through a backdoor adjustment formula in a causal inference mode.
Owner:ZHEJIANG UNIV
Main effect factor analysis method and equipment based on causal model
PendingCN113392579AOvercoming the Problem of IsolationIncrease plausibilityMathematical modelsDesign optimisation/simulationEffect factorAlgorithm
The invention discloses a main effect factor analysis method and equipment based on a causal model. The main effect factor analysis method based on the causal model comprises the following steps: constructing a Bayesian network model, and obtaining direct influence factors of a result node; obtaining an influence path set of influence of each direct influence factor on a result node; when the influence path set comprises indirect influence paths, analyzing the influence degree of the direct influence factors on the result nodes jointly based on the node interaction effect, and otherwise, calculating the influence degree of the direct influence factors on the result nodes directly by adopting an independent influence degree analysis method; and selecting the direct influence factor corresponding to the maximum value from all the influence degrees as a main effect factor. According to the method, the problem of factor isolation of a single-factor analysis method is solved, the main effect factor is extracted on the basis of fully analyzing the linkage interaction influence between the factors, and the reasonability and credibility of the main effect factor analysis conclusion are improved.
Owner:CHINA ACADEMY OF ELECTRONICS & INFORMATION TECH OF CETC
Network security information processing method and system based on causal model
The invention discloses a network security information processing method and system based on a causal model. The method comprises the following steps: acquiring a group of data packets received within a preset time period before the current time; judging whether a plurality of data packets conforming to a preset rule exist in the group of data packets or not, the preset rule being the rule that the data packets appear before a preset type of network attack, and the preset rule being obtained by summarizing data packets obtained before the network attack of the preset type occurs after the network attack of the same preset type occurs; under the condition that the judgment result is that the plurality of data packets conforming to the preset rule appear, obtaining the preset type corresponding to the preset rule; and sending the alarm message. According to the method and the system, the problem in the prior art that the cause of the network attack cannot be known is solved, so that support is provided for improving the security of the network.
Owner:HANGZHOU INNOVATION RES INST OF BEIJING UNIV OF AERONAUTICS & ASTRONAUTICS +1
Nuclear power plant organization behavior reliability assessment method, device and equipment
The invention discloses a nuclear power plant organization behavior reliability assessment method, device and equipment. The method comprises the steps of obtaining an organization error causal modelobtained through scene environment analysis under a preset scene and behavior forming factors influencing organization behaviors in the model and a causal influence relation of the behavior forming factors; obtaining the state level of the behavior forming factor; according to a preset organization behavior reliability assessment model, obtaining a fault probability of the organization behavior tobe assessed by utilizing the state level so as to assess the reliability of the organization behavior, wherein the preset organization behavior reliability assessment model is a model containing corresponding relations between different state level levels of the behavior forming factors and organization behavior fault probabilities. Namely, the state grade level of the behavior forming factor isutilized, the error probability of the to-be-evaluated organization behavior is obtained according to the preset organization behavior reliability evaluation model, quantitative evaluation on the organization behavior reliability is achieved, and theoretical and data support is further provided for prevention of organization errors and risks.
Owner:NANHUA UNIV
Method and apparatus for training causal model
PendingCN108629418AEfficient solutionImprove time and efficiencyMathematical modelsAlgorithmProbabilistic principal component analysis
Embodiments of the disclosure relate to a method and apparatus for training a causal model, and a computer readable storage medium. For example, the method for training the causal model includes steps: establishing the causal model based on a plurality of observation variables and at least one hidden variable, wherein the causal model comprises a first parameter and a second parameter which are tobe determined, the first parameter indicates first relations between the plurality of observation variables, and the second parameter indicates second relations between the at least one hidden variable and the plurality of observation variables; determining the second parameter and a third parameter associated with the first parameter by employing a probability principal component analysis; determining noise of the causal model based on the second parameter and the third parameter; and determining the first parameter based on the noise. The embodiment of the disclosure also provides an apparatus capable of realizing the above method and a computer readable storage medium.
Owner:NEC CORP
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