133 results about "Causal relations" patented technology

A Semantic Processor for the recognition of Cause-Effect relations in natural language documents which includes a Text Preformatter, a Linguistic Analyzer and a Cause-Effect Knowledge Base Generator. The Semantic Processor provides automatic recognition of cause-effect relation both inside single fact and between the facts in arbitrary text documents, where the facts are also automatically extracted from the text in the form of seven-field semantic units. The recognition of Cause-Effect relations is carried out on the basis of linguistic (including semantic) text analysis and a number of recognizing linguistic models built in the form of patterns.

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.

A system and method for automatically creating causal association models of complex information technology (IT) systems. System components and elements are subject to periodic monitoring associated with performance thresholds. Numerical data indicating component state information is converted to Boolean attributes by use of defined or discovered thresholds. Resulting data is accumulated and data mined for component relations within the IT system using association rules induction methods. Models of the system may then be adapted with results generated from the analysis to accurately reflect causal relations among various system components.

A processor architecture for a learning machine is presented which uses a massive array of processing elements having local, recurrent connections to form global associations between functions defined on manifolds. Associations between these functions provide the basis for learning cause-and-effect relationships involving vision, audition, tactile sensation and kinetic motion. Two arbitrary images hold each other in place in a manifold association processor and form the basis of short-term memory.

A processor architecture for a learning machine is presented which uses a massive array of processing elements having local, recurrent connections to form global associations between functions defined on manifolds. Associations between these functions provide the basis for learning cause-and-effect relationships involving vision, audition, tactile sensation and kinetic motion. Two arbitrary images hold each other in place in a manifold association processor and form the basis of short-term memory.

The present invention relates to control, monitoring, and automation. The present invention more specifically relates to pattern-based intelligent control, monitoring and automation. The invention performs pattern-based monitoring. It collects signal data from one or more signals. The signal data define signal data streams. It then transforms each of the signal data streams into trends. It also discovers patterns based on the trends within each signal data stream and / or across the signal data streams. The patterns are optionally used for diagnostics and root cause analysis, online plant monitoring and operation control, plant optimization, and other environments where a causal link or correlation may exist between related inputs, states and / or outputs.

The invention provides an online anomaly monitoring and diagnosis method and system. The method comprises the steps that the causal relation and conditional relation among all signals are defined based on a directed graph model; based on definitions of the signals by the directed graph model, the signals in acquired historical data are classified, and a training set of the historical data is established to perform model training and determine model parameters; and based on real-time data theoretical values of the signals in real-time data obtained after the real-time data acquired through online monitoring is input into a trained model, whether the signals in the real-time data are abnormal is determined. The system adopting the method and a computer readable medium storing a program executing the method are also included. Through the method, the system and the computer readable medium, a complete logic relation and casual relation are constructed for the whole process of industrial production and operation through the directed graph model, and more reasonable, more correct, more accurate and efficient production online anomaly diagnosis prediction is realized in combination with machine learning.

A process for generating a performance model from a functional model for a system including a plurality of distributed hardware and software entities that engage to provide a service to at least one user. The process distributes the representative system requests in a finite number of groups and identifies, for each request group, the corresponding execution flow, formalizes the execution flows by a notation that makes it possible to highlight the causal relationships between the different software entities of the system that are involved in the execution flows and the data characterizing the system's resource consumption, develops an intermediate model, and automates the conversion of the developed intermediate model into a performance model.

The invention discloses a workflow mining method based on subsequent tasks. The workflow mining based on an event log takes the event log as an input and takes a workflow model described by a Petri network as an output result; by the method, event types are introduced to ensure that the workflow log comprises a subsequent task of the current task, and the subsequent task refers to an aggregate of the tasks to which an execute permission is transferred after the current task is finished; and the method comprises the following steps of: (1) setting an initial value for a workflow process model to be mined; (2) analyzing the event log W, and calculating a task set TW, an initial task and an end task TO; (3) calling a relation Preprocess process to obtain a causal relation matrix M2 and a potential concurrency relation and concurrency relation matrix M3; and (4) calculating an initial task relation set XW according to the matrixes M2 and M3. By the invention, an integrated workflow mining method based on the subsequent tasks is formed.

The present disclosure relates to power system sensitivities as computed from power flow parameters and control parameters of a Power Flow Control Device (PFC). To this end, control parameter variations are applied to or generated by a PFC, and comprise variations in a control input u, a control effort e (injected series voltage, inserted series reactance), or a control effect q (power flow, active power transfer, phase-shift, current). A power flow response measuring unit measures a variation of a power flow response such as current, active or apparent power, in a way sufficiently synchronized with the control parameter variation to allow establishing an unambiguous causal relationship or correspondence in the form of a power system sensitivity. The latter may be on-line adapted to continuously reflect an updated aspect of the power system behaviour, and thus enable an improved, fast and reliable power flow control in power systems comprising a meshed power network with two parallel flow paths or corridors connecting two areas or sub-systems.

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.

A method of developing maintenance optimization model includes finding relevant criteria to drive the design towards operational performances at minimal cost for the end user, selecting inputs necessary to assess criteria selected, defining mathematical models to jointly drive the equipment / sub-system / system design and its support towards better supportability, and presenting the maintenance optimization model results to enable the exploration of the cause and effect relationships between design decisions and their operational and support impacts. The selected inputs may cover all the potential factors influencing the criteria values. The mathematical modeling can facilitate leveraging the intuitive “cause and effect” relationship between design and support, and affordability. The maintenance optimization model method, system, and computer program product provides a solid basis for the supportability evaluation of equipment / system / sub-system choices, particularly when integrated in a systematic way into the design process and used from the beginning of the development cycle.

A system and method for identifying, monitoring, and analyzing various trends and patterns of interest within an organization in order to maximize aspects thereof, including, for example, productivity, efficiency, and employee health and safety. The invention utilizes a centralized data repository to accessibly store and maintain data; date gap analysis to avoid aggregation on calender or other artificial boundaries; control chart analysis to allow for easy understanding of the data; workload adjustments to avoid false indicators; tabular and graphical data displays which facilitates identifying anomalous data and monitoring for data quality; and a drill down mechanism for investigating trends and anomalous data points in detail. Analysis may be performed on various normalized data sets and the results simultaneously displayed to allow comparison and easier identification of interrelated variables and, thereby, of cause and effect. The effectiveness of remedies and intervention schemes may also be monitored and analyzed.

An “Eventually Consistent Sharing Model” provides various techniques for using “revision diagrams” to determine both arbitration and visibility of changes or updates to shared data (e.g., data, databases, lists, etc.) without requiring a causally consistent partial order for visibility, and without requiring change or update timestamps for arbitration. In particular, the Eventually Consistent Sharing Model provides fork-join automata based on revision diagrams to track the forking and joining of data versions, thereby tracking updates made to replicas of that data by one or more sources. “Cloud types” are used to define a structure of the shared data that enables fully automatic conflict resolution when updating the shared data. These concepts enable mobile devices (or other computing devices that may periodically go “offline”) to share structured data in cloud-based environments in a manner that provides local data replicas for offline operation while guaranteeing eventually consistent convergence of the data replicas.

A method for constructing a database to deduce a disease and constructing a Bayesian network for U-Health application, the method including the steps of analyzing U-Health information, which includes a disease, a symptom, and a treatment, and constructing a plurality of U-Health ontologies required for service provision, setting a meta-model defining cause-and-effect relationships between the constructed U-Health ontologies and selecting at least two specific U-Health ontologies from among the plurality of U-Health ontologies, setting the selected U-Health ontologies as nodes, and generating a Bayesian network by applying the meta-model to the set nodes.

[Object] To provide a question-answering system capable of appropriately answering to a non-factoid question.[Solution]A question-answering system 30 includes: a related document searching unit 54 responsive to a question, for taking out answer candidates from an object document storage unit 32; an answer candidate extracting unit 56 extracting plausible ones from the answer candidates; a causal relation recognizing unit 60 recognizing causal relation expression included in extracted answer candidates; causal relation relevancy determining unit 64 determining whether or not a recognized causal relation is relevant as an answer; a feature vector generating unit 66 generating a feature vector related to a combination of the question and the searched answer candidate using the results of processing up to the causal relation relevancy determining unit 64; and a feature vector generating unit 66 for calculating, when a feature vector is given, a score indicating a degree of how correct an answer candidate is as an answer to the question, for the question and the answer candidate as a source of the feature vector, and for outputting, as the answer to the question, an answer candidate having the highest score.

Systems, methods and recordable media for facilitating user-guidance of computational analysis and knowledge extraction tools for use with biological data for disambiguation and determination of causation in representations of the data. Systems, methods, tools and computer readable media for user-guided expansion of biological networks and manipulations of the same. Tools for qualitative simulation of network models, for identifying interactions between multiple networks and form maintaining knowledge about multiple alternative biological networks. Combinations of portions of networks may be constructed to provide optimal networks. Networks may be evaluated against experimental data and networks of interest may be identified based on experimental data.

The present invention provides a method for extracting relationships between words in textual data. Initially, a classifier is trained to identify text data having a specific format, such as situation-response or cause-effect, using a training corpus. The classifier receives input identifying components of the text data having the specified format and then extracts features from the text data having the specified format, such as the part of speech for words in the text data, the semantic role of words within the text data and sentence structure. These extracted features are then applied to text data to identify components of the text data which have the specified format. Rules are then extracted from the text data having the specified format.

Conventionally, there is a technique to conduct a cause analysis in which a single evaluation axis, such as a performance time, is used to configure a problem point network. However, in particular, in a system to visualize a management problem in which problems are intricately-intertwined with one another, information on multiple evaluation axes, collected from a business system, are utilized, and units are different for respective evaluation axes. Therefore, it is difficult to simply specify a cause of problem occurrence. In view of this situation, an object of the present invention is to extract and present factor which has a significant influence on the problem occurrence. The present invention is directed to an apparatus which stores causal relation information specifying the causal relation and the relation level between the indexes, business goal achievement level information, and an influence level of the causal relation, calculates the influence level by using the business goal achievement level information and the relation level of the causal relation, specifies as the causal index the index having a large influence, and generates screen information for displaying a graph and / or table relating to the causal index.

The present invention provides a method for extracting relationships between words in textual data. Initially, training relationship data, such as word triplets describing a cause-effect relationship, is received and used to collect additional textual data including the training relationship data. Distributed data collection is used to receive the training data and collect the additional textual data, allowing a broad range of data to be acquired from multiple sources. Syntactic patterns are extracted from the additional textual data and a distributed data source is scanned to extract additional relationship data describing one or more causal relationships using the extracted syntactic patterns. The extracted additional relationship data is then stored, and can be validated by a supervised learning algorithm before storage and used to train a classifier for automatic validation of additional relationship data.

The invention relates to a method for generating a pressure distribution chart of an urban water supply pipeline network in the technical field of monitoring. The method comprises the following steps of: firstly, collecting the pressure data of nodes of a pressure measurement point, a water plant, a pumping station and the like from the water supply pipeline network by utilizing an SCADA (Supervisory Control And Data Acquisition) system; secondly, preprocessing the collected node data; thirdly, carrying out restrictive cluster analysis on a sample of the collected pressure data; fourthly, zoning the water supply pipeline network according to a clustering result, selecting a representative pressure node from each zone so as to represent the pressure level of the zone in which the representative pressure node is in; and finally, displaying different colors according to the pressure value of each zone and outputting the pressure distribution chart of the water supply pipeline network. The method adopted by the invention is simple and has high efficiency and strong robustness. If the accidents of damage and leakage occur in pipelines, the representative pressure nodes still have the representativeness of the pressure values because the pipelines of the nodes in one class are adjacent and a causal relation exists in the pressure.

Described is a novel, new, inexpensive approach to screen, perform early diagnosis (on asymptomatic and symptomatic subjects for example), diagnose, establish root causes, and treat subjects. A series of medical steps, each of which is designed to provide the administering healthcare provider with both subjective and objective risk, health and cause evaluation information provides a guide a practitioner to treatments that prevent, slow, delay, stop, or reverse the chronic disease conditions at the root of their cause. Each step in the process provides intelligence about cause and effect. The sum of the steps, when evaluated based on patient outcome, is the basis of a chronic disease health learning engine that leads to continuous improvement of medical knowledge, disease, and methods of healing and treatments to improve patient outcomes.

Briefly described, embodiments of the present invention pertains to a key performance indicator (KPI)-driven digital genome system or framework as well as various systems and methods of use and interaction therewith. Unlike conventional stand-alone KPI applications or pure-play centralized KPI solutions, embodiments of the present invention provide an automated way to codify the organizational objectives, goals, behavior, and motivations by continuously measuring, correlating, and discovering hidden relationships among various metrics, attributes, causal relationships, and networks display genomic findings via business applications without a priori knowledge of machine learning or statistical techniques.

In one embodiment, a method is contemplated. The method includes exposing at least three dimensions of a protection system to a user. The three dimensions are interrelated. The method further includes receiving user input indicating a modification in a first dimension of the at least three dimensions; and determining an effect of the modification in each other dimension of the at least three dimensions. A computer accessible medium comprising a plurality of instructions which, when executed, implement the method and a system implementing the method are also contemplated.

An imaging apparatus for imaging a heart is provided, wherein the imaging of the heart is improved such that conclusions about regions of the heart having an abnormal behaviour can be made more accurate and more optimal. The imaging apparatus comprises a first site determination unit for determining a first site of the heart comprising a first property type like a fractionated electrogram (70,71,74,75) and a second site determination unit for determining a second site comprising a second property type like a ganglionated plexus (72,73). The first site and the second site are causally related and displayed on a display unit. Since the displayed first and second sites are causally related to each other, a further information is given, i.e. the causal relation, which assists a user in finding regions of the heart showing an abnormal behaviour.

Method and apparatus for determining causal relations between a plurality of intercommunicating nodes. In various embodiments, trace data is input that describe inter-node communication. The trace data may include for each message sent between nodes a timestamp that indicates a time at which the message was sent, a source identifier that identifies a node from which the message was sent, and a destination identifier that identifies a node to which the message was sent. For each of one or more nodes, a determination may be made as to whether one or more causal relations exist between a first set of messages destined to the node and a second set of messages sourced from the node and destined to at least one other node. A causal relation may exist as a function of a probability distribution of delay values that are differences between timestamps of messages in the second set and timestamps of messages in the first set. From the nodes and causal relations a processor-readable representation is generated.

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.

The invention discloses an analyzing method and an analyzing device for operator state assessment reliability. The method comprises the steps of determining a plurality of PSFs, wherein the PSFs are used for state assessment; determining the incidence relation between each PSF and all the other PSFs in the plurality of PSFs except the PSF itself and the incidence relation between each PSF and a state assessment reliability node; and according to the incidence relations, establishing an analyzing model of the operator state assessment reliability based on the PSF causal relations so as to analyze the reliability of an operator. Through the application of the analyzing method and the analyzing device, the problems that in the prior art, due to the fact that influences of situational environmental factors to the operator and influence causal relations of the factors are not considered, the possibility of repetitive computation of the influences is brought, and false assessment can be caused to state assessment miss probability are solved, therefore a qualitative and quantitative method is provided for analyzing the operator state assessment reliability and strategies are provided for lowering operator state assessment failure probability for an electric power plant.

The invention discloses a power electronic system fault diagnosis method based on a bond graph. The power electronic system fault diagnosis method comprises the following steps of (1) combining a modulation transformer MTF of a standard bond graph element and a resistive element R to construct a switch bond graph element with a fixed causal relationship; (2) constructing a behavior bond graph model of a power electronic system to simulate running states of the system during normal working and under failure; (3) constructing a global analytical redundancy relation with an independent structure in the model, obtaining a circuit fault feature matrix and system running residue set, and comparing the set with a fault feature matrix to determine the health state of the system and position a faulted element. A bond graph modeling and fault diagnosis method provided by the invention can be used for performing uniform modeling and fault diagnosis on a multi-energy-domain system such as electromechanics, so that the problem that mistakes are easily made in the conventional method for respectively modeling different energy domains and combining energy domain models is solved; a solution based on a bond graph method is supplied to multi-energy-domain system modeling and fault diagnosis based on a model method.