645 results about "Multi variable" patented technology

A diagnostic tool automatically collects and stores data indicative of a variability parameter, a mode parameter, a status parameter and a limit parameter for a multi-variable function block associated with one or more devices or loops within a process control system, processes the collected data to determine which devices, loops or function blocks have problems that result in reduced performance of the process control system, displays a list of detected problems to an operator and then suggests the use of other, more specific diagnostic tools to further pinpoint or correct the problems. When the diagnostic tool recommends and executes a data intensive application as the further diagnostic tool, it automatically configures a controller of the process control network to collect the data needed for such a tool.

An computer implemented apparatus and method are disclosed for defining areas of a field in which a crop or other vegetation is grown based on their selective ability to grow such vegetation through a growing season, or some shorter preselected time period. The method includes making a number of temporally separated measurements through air borne imaging of a field, registering the data to the geography of the field and each other, normalizing the data including converting the data to a vegetative index indicative to the presence of vegetation in the field, comparing the data to identify clusters of like value, and classifying the clusters and images to learn how the different field areas responded in growing vegetation through the season. With this method, the field may be segregated into a number of like areas called crop response zones which exhibit similar vegetative growth characteristics as an aid to a grower in his decision making regarding how to maximize yield in his field.

A method for detecting faulty operation of a multi-variable system is described. The method includes receiving operational data from a plurality of components of the multi-variable system and processing the operational data in accordance with a plurality of dynamic machine learning fault detection models to generate a plurality of fault detection results. Each fault detection model uses a plurality of variables to model one or more components of the multi-variable system and is adapted to detect normal or faulty operation of an associated component or set of components of the multi-variable system. The plurality of fault detection results are output.

A method of analysis including an intrinsically valid class of statistical methods for dealing with multivariate ordinal data. A decision support system that can (1) provide automated decision support in a transparent fashion (2) optionally be controlled by a decision maker, (3) provide for an evidence acquisition concept, including automatically increasing the content of an underlying database, and (4) provide a computationally efficient interactive distributed environment. The method is exemplified in the context of assisted diagnostic support.

A model-free adaptive controller is disclosed, which uses a dynamic artificial neural network with a learning algorithm to control any single-variable or multivariable open-loop stable, controllable, and consistently direct-acting or reverse-acting industrial process without requiring any manual tuning, quantitative knowledge of the process, or process identifiers. The need for process knowledge is avoided by substituting 1 for the actual sensitivity function .differential.y(t) / .differential.u(t) of the process.

The present invention generally relates to computer databases and, more particularly, to data mining and knowledge discovery. The invention specifically relates to a method for constructing segmentation-based predictive models, such as decision-tree classifiers, wherein data records are partitioned into a plurality of segments and separate predictive models are constructed for each segment. The present invention contemplates a computerized method for automatically building segmentation-based predictive models that substantially improves upon the modeling capabilities of decision trees and related technologies, and that automatically produces models that are competitive with, if not better than, those produced by data analysts and applied statisticians using traditional, labor-intensive statistical techniques. The invention achieves these properties by performing segmentation and multivariate statistical modeling within each segment simultaneously. Segments are constructed so as to maximize the accuracies of the predictive models within each segment. Simultaneously, the multivariate statistical models within each segment are refined so as to maximize their respective predictive accuracies.

Disclosed herein is a computer-implemented method for improving a lithographic process for imaging a portion of a design layout onto a substrate using a lithographic projection apparatus, the method comprising defining a multi-variable cost function, the multi-variable cost function being a function of a stochastic effect of the lithographic process.

The invention provides a system, device and method that can control the rates of seed dispensation and fertilizer application as a function of forward speed as well as across all rows. Thus, the system and control device allow control of each individual row unit, whereby dispensation and application are effected, by a central controller which controls a plurality of pulse width modulation valves, each of which controls a hydraulic motor with an integrated rpm reduction and which drives a stub drive shaft which rotates at the proper speed in order to provide the desired seed and fertilizer metering. Each of the stub drive shafts may rotate at completely different speeds and / or may be shut down while the remaining stub drive shafts rotate at the desired speed. The integrated rpm reduction within each hydraulic motor allows very fine rotational control of the dispensation and application rates.

This invention discloses a sort of method of the PID parameter setting of the coordinated control system of the cell-system generating set which belongs to this area of the control technique of the set. This method identifies the multivariable dynamic mathematic model of the unit plant according to the universal structural style. Used the series decoupling principle and the two-degree of freedom control structure the coordinated control system of the unit plant which has the PID form is thought out based this model, thereby the each setting step of the related parameter are get in this system by combining the theoretical derivation and the engineering test. This invention has this character in that the consecution is clear; the practicability is good, and it fits to engineering apply. The engineering technical personnel structures the corresponding coordinated control system expediently, and combines the engineering test aimed at the unit plant of various group, it sets out the related parameter of this system availably.

The invention discloses a power system low-frequency oscillation mechanism analysis method based on micro-disturbance signal oscillation mode recognition. The method comprises the following steps of: providing a basis for judgment of oscillation mechanism types of power grid low-frequency oscillation accidents by using oscillation components provided by micro-disturbance recognition result in the process of a low-frequency oscillation accident; timely discovering a system running weak link and a potential oscillation source by performing statistic analysis on an entire-grid low-frequency oscillation mode oscillation frequency and damping ratio result which is calculated by micro-disturbance recognition in the given running time period; and establishing a corresponding relation between entire-grid oscillation mode characteristic change and system running condition parameter change to provide a basis for system running adjustment by performing multi-variable association analysis on the entire-grid low-frequency oscillation mode oscillation frequency and damping ratio result which is calculated by micro-disturbance recognition in the given running time period and the system running condition parameter.

A system and method are provided for enabling a user to search for documents that the user has previously viewed on its local machine. The system includes three main components: the desktop integration module, the index module, and the graphical user interface module. The desktop integration module is an application which monitors documents with which the user interacts for predetermined events, and obtains content data and metadata from the monitored documents. The index module indexes the content data and metadata received from the desktop integration module. The graphical user interface module then permits a user to utilize the desktop integration module and index module by allowing a user to search for a document.

A gas turbine engine has, in flow series, a compressor section, a combustor, and a turbine section. The gas turbine engine further has a system (i) for cooling the turbine section and (ii) for providing tip clearance control between turbine blades of the turbine section and a plurality of circumferentially distributed segments which form an annular shroud surrounding the outer tips of the turbine blades. The system includes a turbine section cooling sub-system which diverts a first cooling air flow received from the compressor section to a heat exchanger and then to the turbine section to cool components thereof. The first cooling air flow by-passes the combustor and is cooled in the heat exchanger. The turbine section cooling subsystem has a first valve arrangement which regulates the first cooling air flow. The system further includes a tip clearance control sub-system which supplies a second cooling air flow to an engine case to which the segments are mounted. The second cooling air flow regulates thermal expansion of the case and thereby controls the clearance between the segments and the outer tips. The tip clearance control sub-system has a second valve arrangement which regulates the second cooling air flow. The system further includes a closed-loop controller which issues first and second demand signals to respectively the first and the second valve arrangements. Each of the first and second demand signals are determined on the basis of: (i) a value of the first demand signal at a previous time step, and a measurement or estimate of turbine section component temperature, and (ii) a value of the second demand signal at a previous time step, and a measurement or estimate of tip clearance.

An adaptive predictive expert control system for controlling single-input single-output, or multi-variable time-variant processes with known or unknown parameters and with or without time delay, is disclosed. The adaptive predictive expert control system of the present invention adds an expert block into the operation of previously known adaptive predictive control systems. This expert block based on rules and in the evolution of the process variables, determines and / or modifies the operation of the driver block, control block and adaptive feedback mechanism of the previous art, in order to improve the performance, robustness and stability of the overall control system.

A general, computational-mathematical modeling method for the solution of large, boundary-coupled transport problems involving the flow of mass, momentum, energy or subatomic particles is disclosed. The method employs a modeling processor that extracts a matrix operator equation (or set of equations) from a numerical transport code (NTC). The outputs of software codes, available for modeling physical problems governed by conservation laws in the form of differential equations, can be processed into closed-form operator equations with the method. Included is a numerical transport code functionalization (NTCF) model which can be determined numerically, based on a system of solutions of an NTC, evaluating outputs for a given set of inputs. The NTCF model is a linear or nonlinear, multi-variable operator equation or set of such equations. The NTCF model defines relationships between general, time-variable inputs and outputs, some known and some unknown, considered as boundary values. The user of an NTCF model can directly work with the processed model output, instead of running the original numerical code in general applications of a boundary-value problem. The numerical transport code functionalization model can be employed as a surrogate for representing the numerical transport code to provide a solution to the transport problem. The invention enables modeling efficiency and availability to be increased, while computational complexity and cost decreased. Computational times for complex modeling problems can, in some cases, be dramatially reduced, for example by several orders of magnitude.

A system and method for deriving semiconductor manufacturing process corners using surrogate simulations is disclosed. The method may be used to determine individual performance metric yields, the number of out-of-specification conditions for a given number of simulation samples, and a total yield prediction for simultaneous multi-variable conditions. A surrogate simulation model, such as a Response Surface Model, may be generated from circuit simulation data or parametric data measurements and may be executed using a large number of multi-variable sample points to determine process corners defining yield limits for a device. The model may also be used to simulate process shifts and exaggerated input ranges for critical device parameters. In some embodiments, the derived process corners may better represent physically possible worst-case process corners than traditional general-purpose process corners, and may address differences in process sensitivities for individual circuits of the device.

Apparatuses and methods for model quality estimation and model adaptation in multivariable process control are disclosed. A method for updating a multiple input multiple output (MIMO) dynamical model of a process includes perturbing the process, auditing the controller model, identifying poor performing submodels and re-testing the relevant process variables, re-identifying submodels and adapting the model online while the process continues to operate within normal operating parameters. An apparatus comprises an online multivariable controller, a tester, a database to store data corresponding to manipulated variables and controlled variables, and a performance diagnosis module configured to identify problematic submodels and adapt a model used by the controller.

Systems methods and recordable media for predicting multi-variable outcomes based on multi-variable inputs. Additionally, the models described can be used to predict the multi-variable inputs themselves, based on the multi-variable inputs, providing a smoothing function, acting as a noise filter. Both multi-variable inputs and multi-variable outputs may be simultaneously predicted, based upon the multi-variable inputs. The models find a critical subset of data points, or “tent poles” to optimally model all outcome variables simultaneously to leverage communalities among outcomes.

The invention discloses a fault diagnosis-based rail transit vehicle bearing residual life prediction method. The method includes the following steps that: S100, multi-angle feature extraction and fusion are performed; S200, cyclostationary theory-based fault diagnosis is performed; S300, multi-variable model training is performed, so that multi-variable model-based fault isolation is realized; and S400, based on a semi-supervised algorithm, a BP neural network algorithm and a support vector regression algorithm are used in combination to perform fault diagnosis-based residual life prediction. With the fault diagnosis-based rail transit vehicle bearing residual life prediction method of the invention adopted, fault detection, fault isolation and residual life prediction at the early stage of the fault of a bearing are realized, and a basis is provided for the formulation of vehicle bearing maintenance decision making.

The invention discloses a batch process failure monitoring method based on AR-PCA (Autoregressive Principal Component Analysis). Through the batch process failure monitoring method, the batch process with strong dynamics can be monitored online; when monitoring the bath process, a conventional MPCA (Multiway Principal Component Analysis) does not take corresponding self-correlation and mutual correlation of variables due to the existence of various random noises and interferences into account, so that a large quantity of false alarm is generated in the online monitoring process. The batch process failure monitoring method comprises the following steps: firstly, building a multi-variable autoregressive (AR) model according to measurement variables, recognizing a model coefficient matrix by using a PLS (Partial Least Squares) method and recognizing a model order by using an AIC (Akaike Information Criterion); and then building a PCA model for a residual error of the AR model. Meanwhile, training data is introduced when a new bath of data is monitored online through the algorithm, so that the monitoring effect of the algorithm is improved. Through the batch process failure monitoring method, the defect of a large quantity of false alarm of the conventional MPCA method during the process of monitoring the batch process with strong dynamics can be made up; and the batch process failure monitoring method is of great significance to monitoring of an actual bath production process.

The invention discloses a detection method of an abnormal event of multi-variable water quality parameter time sequence data. The detection method comprises the following steps: firstly, inputting a plurality of water quality parameter models; training and constructing a data driven predication model (BP model); analyzing multi-variable water quality time sequence data in a water supply pipe net and estimating the model; secondly, predicating through the BP model to obtain a predicated value of water quality data; comparing an actually measured value of a current state and the predicated value obtained by the predication model and carrying out error estimation and classification analysis, so as to determine a single-variable parameter abnormal event; classifying based on an error counting result, and updating and determining the event probability of single-variable water quality parameters through sequential Bayesian updating; carrying out multi-variable fusion decision-making and fusing information from a plurality of water quality monitoring indexes; providing a unified decision-making result and determining whether the water supply pipe net has the abnormal event on a specific node or not.

The invention provides a water quality prediction method, which comprises the following steps of building a water quality prediction model on the basis of a deep belief network and a long short time memory network; training a water quality prediction model on the basis of the historical water quality time sequence data and weather data; predicting the water quality on the basis of the water quality prediction model. The invention also provides a water quality prediction device, which comprises a building module, a training module and a prediction module. The water quality prediction method anddevice provided by the invention have the advantages that the deep belief network and the long short time memory network are introduced into the water quality prediction model; a great amount of water quality time sequence data and weather data are processed by using the deep belief network; the relationship between all factors is mined; the groundwork is laid for high-precision prediction difficult to realize the multi-variable nonlinear parameter data; on the basis of the deep belief network, the mass water quality time sequence data and weather data processing results are predicted on thebasis by studying the long-time relationship between the water body quality and the time sequence by the long short time memory network; the optimum prediction effect is obtained.

A graphic user interface facilitates the hierarchical analysis of timing parameters related to network-based applications. At the top level of the hierarchy, the user is presented a summary of the delays incurred while running an application, or while simulating the running of an application, organized by delay categories, including processing delays at each node, as well as propagation delays at each link between nodes. The interface enables a user to “drill down” into lower levels of the timing information hierarchy by ‘clicking’ on currently displayed information. The information is presented in a form most appropriate to the level of analysis. The presentation forms include, for example, pie-charts, multi-variable timing diagrams (in both absolute and relative forms), data exchange charts, and so on, and ‘zoom’ capabilities are provided as appropriate to the particular display form.

Methods for Markov boundary discovery are important recent developments in pattern recognition and applied statistics, primarily because they offer a principled solution to the variable / feature selection problem and give insight about local causal structure. Currently there exist two major local method families for identification of Markov boundaries from data: methods that directly implement the definition of the Markov boundary and newer compositional Markov boundary methods that are more sample efficient and thus often more accurate in practical applications. However, in the datasets with hidden (i.e., unmeasured or unobserved) variables compositional Markov boundary methods may miss some Markov boundary members. The present invention circumvents this limitation of the compositional Markov boundary methods and proposes a new method that can discover Markov boundaries from the datasets with hidden variables and do so in a much more sample efficient manner than methods that directly implement the definition of the Markov boundary. In general, the inventive method transforms a dataset with many variables into a minimal reduced dataset where all variables are needed for optimal prediction of some response variable. The power of the invention was empirically demonstrated with data generated by Bayesian networks and with 13 real datasets from a diversity of application domains.

Systems for receiving, analyzing, and organizing audio content contained within a plurality of media files are disclosed. The systems generally include a server that is configured to receive, index, and store a plurality of media files, which are received by the server from a plurality of sources, within at least one database in communication with the server. In addition, the server is configured to make one or more of the media files accessible to and searchable by, one or more persons other than the original sources of such media files. Still further, the server may be configured to organize audio content included within each of the plurality of media files into bipartite graphs; segment media files into parts that exhibit similar attributes; extract and present meta data to a user that pertain to each media file; and employ multi-variable ranking methods to prioritize media file search results.

The invention discloses a traffic flow three parameter real time prediction method taking regard of space-time correlation. According to the method, on the basis of acquiring traffic flow rate, speed and occupancy data of a target section and upstream and downstream sections of the target section, a state space model for multivariable short time prediction of traffic flow three parameters is established; according to spatial correlation of various traffic variables at different data acquisition sections, an observation equation of the state space model is established; according to time autocorrelation and cross correlation of the multiple traffic variables at one same data acquisition section, a state equation of the state space model is established; prediction and iteration update of the traffic flow three parameters are realized by employing the Kalman filtering algorithm. The method makes full use of the spatial correlation of the traffic flow three parameters at the different data acquisition sections, the time autocorrelation and the cross correlation of the different traffic variables at one same data acquisition section, the multivariable prediction algorithm is employed, and thereby traffic flow short time prediction accuracy is facilitated.

The invention relates to the technical field of unmanned aircraft control, and provides a finite-time track and attitude cooperative control algorithm based on multi-variable interference compensation. The defect in the prior art that a traditional quad-rotor unmanned helicopter is poor in control precision, slow in response speed and not ideal in anti-interference effect can be overcome. Meanwhile, the high-precision fast and stable tracking control problem for the track and the posture of the quad-rotor unmanned helicopter under the influence of comprehensive interferences can be solved. Asa result, the safety and the reliability of the system are enhanced. The track and attitude cooperative control method for the quad-rotor unmanned helicopter based on multi-variable interference compensation comprises the following steps of 1, designing a track controller-interference compensator; 2, resolving the attitude; 3, designing an attitude controller-interference compensator. The method is mainly applied to the control occasions of unmanned aircrafts.

The invention discloses a method for early classifying imbalance multi-variable time sequence data. The method comprises the following steps: carrying out the under-sampling for a large category of category data sets according to an imbalance ratio for the inter-category data scale imbalance problem into a plurality of subsets, and combining the subsets with a small category of category data to form a plurality of training subsets; extracting and selecting core features of each training subset, and establishing a sub-classifier based on a rule according to the core features, wherein the feature selection process is realized in a clustering way in order to solve the imbalance problem of the data scale of the intra-category sub-concepts so as to guarantee the diversity of the core features; and solving the weight of a classifying effect of data in the training set by utilizing the sub-classifier on the basis of each sub-classifier, and establishing an integrated classifier. The classifier can solve the multi-variable time sequence classifying problem of the imbalance data set, the accuracy is relatively high, and the earliness degree is good.

The invention discloses an intelligent building microgrid power utilization behavior identification method. The intelligent building microgrid power utilization behavior identification method is characterized in that power utilization big data is obtained through an intelligent building microgrid power utilization data acquisition terminal and monitoring of each environment sensor; then the obtained data is subjected to pretreatments (data integration, data filling and feature normalization); the load feature parameter of each powered device is extracted from a pretreated data sample by means of a kernel principal component analysis method; the environment factor of each power utilization behavior feature of the user and the contribution rates of different powered devices, i.e., the feature weight of each influence factor of fuzzy C-mean value clustering are determined by means of a multi-variable multi-scale sample entropy weight method; finally the obtained feature data set is subjected to cluster analysis through the fuzzy C-mean value clustering method and the user power utilization behavior identification accuracy and rapidity can be improved. According to the invention, the building microgrid user power utilization behavior can be effectively identified, and a basis and reference can be provided for scheduling of the building microgrid power utilization load by the power grid, and furthermore the fact that the user saves power in life, and power wasting behaviors are reduced is promoted.