632 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.

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.

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.

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.

A method and apparatus for automatically updating the value of a bid variable or the total bid for a multi-variable bid in an online auction is disclosed. The method includes displaying an automatically adjustable mechanism interfaced to a bidder. Thereafter, the automatic bid adjustments are calculated using the mechanism.

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.

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 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 present disclosure is directed toward systems and methods for identifying contributing factors associated with a multi-variable metric anomaly. One or more embodiments described herein identify one or more contributing factors that led to an anomaly in a multi-variable metric by calculating linearizing weights such that the total deviation in the multi-variable metric can be written as a weighted sum of deviations for dimension elements associated with the multi-variable metric.

A multi-hole adjustable multi-variable-load hydraulic buffer comprises a base. An inner cylinder and the outer cylinder are concentrically and rigidly connected with the base. The inner cylinder is provided with inner cylinder damping holes in the circumferential direction. A reset spring is connected with a piston and the bottom of the inner cylinder. A piston rod is connected with the piston. Piston damping holes are formed in the outer wall of the piston. An adjudging knob is arranged at the front end of the piston rod. A locating pin penetrates through the outer cylinder and an end cover to be matched with a guide groove in the piston rod, and the rotation direction of the piston is fixed through the guide groove of the piston rod. According to the changing curve of loads, the damping holes according with the changing rule of the loads are formed in the wall surface of the inner cylinder, and the piston damping holes are formed along the outer wall of the piston; in the buffering process, a damping curve consistent with the changing curve of the loads is formed, and uniform buffering is achieved. Through the adjusting knob and the guide groove in the front end face of the piston rod, the rotation direction of the piston can be adjusted, the matching rule of the damping holes in the wall surface of the inner cylinder and the damping holes in the piston can be changed, in this way, the damping curve of the buffer is changed, and different loads are buffered.