1378 results about "Linear model" patented technology

A system and method for synchronizing a local clock to a reference clock using a linear model of the clock error between the local clock and the reference clock is disclosed. In one embodiment, a double-exponential smoothing process is used in conjunction with the linear model to estimate a frequency offset by which the frequency of an oscillator of the local clock is adjusted. Also disclosed herein is a phased-lock loop (PLL) adapted to synchronize a local clock with a reference clock using the double-exponential smoothing process, as well as a system implementing the PLL for timing the playout of data received from a transmitter.

A system and method for synchronizing a local clock to a reference clock using a linear model of the error between the local clock and the reference clock is disclosed. In one embodiment, a direct smoothing process is used in conjunction with the linear model to estimate a frequency offset by which the frequency of an oscillator of the local clock is adjusted. Also disclosed herein is a phased-lock loop (PLL) adapted to synchronize a local clock with a reference clock using the direct smoothing process, as well as a system implementing the PLL for timing the playout of data received from a transmitter.

A methodology for process modeling and control and the software system implementation of this methodology, which includes a rigorous, nonlinear process simulation model, the generation of appropriate linear models derived from the rigorous model, and an adaptive, linear model predictive controller (MPC) that utilizes the derived linear models. A state space, multivariable, model predictive controller (MPC) is the preferred choice for the MPC since the nonlinear simulation model is analytically translated into a set of linear state equations and thus simplifies the translation of the linearized simulation equations to the modeling format required by the controller. Various other MPC modeling forms such as transfer functions, impulse response coefficients, and step response coefficients may also be used. The methodology is very general in that any model predictive controller using one of the above modeling forms can be used as the controller. The methodology also includes various modules that improve reliability and performance. For example, there is a data pretreatment module used to pre-process the plant measurements for gross error detection. A data reconciliation and parameter estimation module is then used to correct for instrumentation errors and to adjust model parameters based on current operating conditions. The full-order state space model can be reduced by the order reduction module to obtain fewer states for the controller model. Automated MPC tuning is also provided to improve control performance.

A system and method for historical database training of non-linear models for use in electronic commerce. The non-linear model is trained with training sets from a stream of electronic commerce data. The system detects availability of new training data, and constructs a training set from the corresponding input data. Over time, many training sets are presented to the non-linear model. When multiple presentations are needed to effectively train the non-linear model, a buffer of training sets is filled and updated as new training data becomes available. Once the buffer is full, a new training set bumps the oldest training set from the buffer. The training sets are presented one or more times each time a new training set is constructed. An historical database may be used to construct training sets for the non-linear model. The non-linear model may be trained retrospectively by searching the historical database and constructing training sets.

The invention relates to a composite amplifier (500) based on a main amplifier (510) and an auxiliary amplifier (520), and compensation for non-linear amplifier behavior by means of respective non-linear models (570, 575) of parasitic. In order to provide proper excitation of the non-linear models. a filter network (560) based on a linear model of the output network of the composite amplifier is provided. The linear filter network (550) basically determines ideal output node voltages, which are used as input to the respective non-linear models for generating appropriate compensation signals. The compensation signals are finally merged into the input signals of the respective sub-amplifiers (510, 520), thus effectively compensating for the effects of the non-linear parasitics.

Constant-bitrate rate control is very useful for real-time video transmission. However, it is difficult to realize a good CBR rate control in an MPEG4-AVC video codec. According to the invention, an adapted ρ-domain rate control processing for real-time rate control is applied, whereby ρ is the percentage of zero amplitude transform coefficients following quantisation. A couple of tables are used for mapping between the quantization parameters and ρ. A detailed analysis at transform-level is carried out in order to get the slope of the relationship between the bitrate and ρ. The slope of the linear model is constrained by a look-ahead procedure with respect to the neighbour frames or blocks.

A bid evaluation tool is provided for evaluating proposed bids for commodity sales contracts. The tool displays historical price data and forecasts in a graph. A user of the tool defines a date range for a proposed contract by entering starting and ending dates for the contract. The tool analyzes price data for the date range and generates a corresponding proposed bid for the date range. A bid window may be used to gather user inputs for the tool. The user can select between multiple bid pricing models for the bid evaluation tool to use when processing the price data to produce the proposed bid. Bid models for the tool include models such as a flat average model, a linear fit model, and an offset model. The user can adjust which historical data is included in the graph and can select which data set is used to generate bids.

The invention discloses an article information recommending method and device. The method comprises the steps of obtaining attribute information and user behavior data of an access user when an article access request is received; obtaining a corresponding candidate article set; determining articles satisfying preset conditions in the candidate article set based on a similarity matrix, the attribute information and the user behavior data, wherein the similarity matrix is used for indicating the similarities among the candidate articles, and the similarities between the candidate articles and the attribute information; and recommending the information of the articles satisfying the preset conditions to the access user. Through adoption of the similarity matrix, the attribute information of the access user and recent different click and consumption behaviors to the articles, intention prediction is carried out on user access; and therefore, the articles suitable for the user are determined and recommended to the user. Compared with the mode of carrying out recommendation through prediction of the click-through-rate scores of the user to the articles based on a linear model, the method and the device have the advantages of improving individuation of the recommendation results and improving the accuracy of the recommendation results.

A system and method for preprocessing input data to a non-linear model for use in electronic commerce (e-commerce). The non-linear model may include a set of parameters that define the representation of an e-commerce system. The non-linear model may operate in training or run-time mode. A data preprocessor may be provided for preprocessing received data in accordance with predetermined preprocessing parameters and outputting preprocessed data. The data preprocessor may include an input buffer for receiving and storing the input data, where the input data may be on different time scales. A time merge device may select a predetermined time scale and reconcile the input data so that all of the input data are placed on the same time scale. An output device may output the reconciled data from the time merge device as preprocessed data. The preprocessed data may then be used as input data to the non-linear model.

A method and system for assessing electrode-tissue contact before the delivery of ablation energy. The method may generally include determining a difference between a maximum impedance magnitude at a low frequency for a given electrode and an absolute minimum impedance magnitude at the low frequency across all electrodes, determining a difference between a maximum impedance magnitude at a high frequency for a given electrode and an absolute minimum impedance magnitude at the high frequency across all electrodes, and determining a difference between a maximum impedance phase at the high frequency for a given electrode and an absolute minimum impedance phase at the high frequency across all electrodes. These differences may be correlated to one another using a linear model, the results of which determining whether the given electrode is in contact or not in contact with tissue.

A method and a measuring instrument for determining the position of an edge to be measured on a pattern on a substrate are described. A complete, nonlinear model intensity profile, which identifies the edge to be measured, of a model edge is ascertained and stored, and a desired edge position xk is defined therein with subpixel accuracy. A camera image of the substrate having the edge to be measured is acquired, and a one-dimensional measured intensity profile of the edge to be measured is determined therefrom. The model intensity profile is identified in the measured intensity profile with an indication of its location xm relative to a reference point. The desired position p of the edge to be measured is determined with subpixel accuracy as p=xm+xk.

A rate based model predictive controller for air path control for a diesel engine regulates turbine lift and EGR valve flow rate to specified set points by coordinated control of intake manifold air pressure and EGR valve flow rate. The controller utilizes a rate-based reduced order linear model for model predictive control.

A system and method for on-line training of a non-linear model for use in electronic commerce. The non-linear model is trained with training sets from a stream of process data. The system detects availability of new training data, and constructs a training set from the corresponding input data. Over time, many training sets are presented to the non-linear model. When multiple presentations are needed to effectively train the non-linear model, a buffer of training sets is filled and updated as new training data becomes available. Once the buffer is full, a new training set bumps the oldest training set from the buffer. The training sets are presented one or more times each time a new training set is constructed. An historical database may be used to construct training sets for the non-linear model. The non-linear model may be trained retrospectively by searching the historical database and constructing training sets.

A linear model based generalized predictive control system controls the molten pool temperature during a Direct Metal Deposition (DMD) process. The molten pool temperature is monitored by a two-color pyrometer. A single-input single-output linear system that describes the dynamics between the molten pool temperature and the laser power is identified and validated. The incremental generalized predictive control algorithm with Kalman filter estimation is used to control the molten pool temperature.

The invention discloses a depth map intra prediction method based on a linear model. The gray values and coordinates of the previous line and left row of adjacent pixels of a current coded block are utilized to determine a linear model parameter; and according to the parameter and the pixel coordinate of the current coded block, the pixel gray value of the current coded block is predicted. According to the spatial character of a depth map, the depth map intra prediction method based on a linear model has the advantages of accurate prediction; meanwhile, because the previous line and left row of adjacent pixels of the current coded block are adopted to calculate the model parameter, a coding end does not need to code the model parameter; and a decoding end can directly determine the model parameter. The depth map intra prediction method can be applied to the coding standard of a three-dimensional video.

The invention discloses a nonlinear robust controller design method based on back-stepping and sliding mode control technologies and aimed at a nonlinear model of a quad-rotor unmanned plane. A sliding mode controller of a speed-constant reaching law is designed to an attitude angle system of the quad-rotor unmanned plane, and rapid tracking for the attitude angle is ensured. To realize track tracking for the spatial position of the quad-rotor unmanned plane, a sliding mode surface and a virtual control quantity are constructed according to a stepping back control idea to realize nonlinear control law design and system stability design from the kernel to the external layer of a system. After an equation of the related virtual control quantity is obtained, an expected track value of the attitude angle is obtained by solving the equation via arithmetic inverse operation, and a design method of the speed-constant reaching law is used to determine a final input control law of the quad-rotor unmanned plane system. According to the method of the invention, the characteristic that sliding mode control is uncertain for the model and insensitive to external interference is utilized, robust trace tracking for the nonlinear quad-rotor unmanned plane can be controlled under interference.

The invention discloses a robust control method of burden error of pitch concrete stirring device, which comprises first building an input / output artificial neutral network model of three-layer structure, while the input layer has one neuron, the middle layer has five neurons and the output layer has one neuron, processing dynamic prediction and learning. The invention builds the non-linear model of flux and fly ash amount to be compared with a linear model of fixed parameters via continuous learning and adjustment, to adapt the change of external parameters, thereby effectively controlling the burden error of pitch mixture stirring device, reaching +-2% burden error as maximum and improving the burden accuracy of pitch mixture stirring device.

An aero-engine online optimization and multivariable control design method based on model predictive control achieves the control and online optimization of multiple variables of an aero-engine undera constraint and according to requirements of thrust and speed. A control system consists of two parts. The first part is a prediction model acquisition layer. Based on the actual working state of each control cycle and the external environmental parameters of the aero-engine, an engine small deviation linear model near different steady-state points is continuously established, and model parameters are provided for a model predictive controller. The second part is a control law decision layer. A closed-loop structure is formed by the model predictive controller and an external output feedback.The model predictive controller, based on an engine model in the current state, a control command and relevant constraint limits, determines the output of the controller at the next moment by solvinga linear optimization problem. The external output feedback introduces the aero-engine actual output into the decision on future controlled quantity of the controller to compensate for the influenceof model mismatch and external disturbances.

The invention discloses a method for reconstructing an outer outline polygon of a building based on multivariate data. The method comprises the following steps of: respectively dividing DSM (Design Standards Manual) data and image data so as to obtain a mask image of an interest region of the building and an image dividing object; combining the mask image with the image dividing object so as to obtain a complete building object; carrying out boundary tracing on the building object so as to obtain curves of the building; using points corresponding to local maximum curvature values of the curves as angular points; connecting the angular points in sequence so as to obtain the outline polygon of the building; dividing the building object into regions by using a hierarchical clustering method and calculating the main direction of the building; establishing a linear model of the polygon of the building and correcting and regularizing the linear model of the outline of the building with the combination of the main direction of the building and gradient information of the image data; calculating an intersection point of every two adjacent straight line sections by using the linear model of each line section of the polygon; and by taking the intersection points as the angular points, connecting the angular points in sequence so as to form the final polygon of the building. According to the method, the DSM data is organically combined with the image data, the data are complementary to each other in the whole process, so that the problem of reconstructing the polygon of the outline of the building is solved well, and the method has very strong robustness in the two-dimensional outline modeling aspect of the building.

The invention provides a silicon chip aligning semaphore acquisition and processing system. The system can achieve the functions of gathering, filtering, modulating and demodulation from light signal to electric signal in real-time, synchronizing gathering and processing of location data, satisfy silicon chip alignment function of light scribing device through fitting processing of sample-taking signal. The invention also provides a processing method fir silicon chip aligning signal. The method adapts high order non-linearity signal fitting mould to obtain firstly envelope parameter of sample-taking signal in multiplestep fitting processing method and translates high order non-linearity signal fitting mould into 1 order linearity mould to obtain phase information of sample-taking signal.

The invention discloses a credit assessment model training method, and a credit assessment method and apparatus. The training method comprises the steps of obtaining original training behavior data of a training user in a business system; extracting original training features in the original training behavior data; performing feature combination on the original training features according to a GBDT model to generate corresponding training cross combination features; and performing training on a logistic regression model according to the training cross combination features to build a credit assessment model. According to the method, the original training features are trained through the nonlinear GBDT model to generate the corresponding training cross combination features, and the linear LR model is trained through the training cross combination features to build the credit assessment model, so that the credit assessment model has not only high performance of the nonlinear model but also interpretability of the linear model.

Embodiments of the present invention provide a method and a device for predicting video image components. The method comprises: obtaining a luminance component value of an image block; obtaining a first numerical value a3 through a linear regression technology based on a luminance component value and a chrominance component value of a sampling point in an application template, changing numerical precision after compensating the first numerical value a3 so as to obtain a first parameter alpha and a third parameter k; obtaining a second parameter beta of a linear model based on the first parameter alpha and the third parameter k; obtaining a predicted value of a chrominance component of the image block through the linear model based on the luminance component value of the image block, the first parameter alpha, the third parameter k and the second parameter beta. The technical solution enhances encoding performance by improving a parameter in the linear model used at the time of predicting the video image components, and meanwhile lowers complexity of the device using the predicting method.

A method for defining a sliding mode control system for a turbocharger system is provided. The method includes perturbing a turbocharger actuator and measuring turbocharger boost pressure output from the perturbation. The perturbation and measurements are repeated for a plurality of states of operation of the turbocharger. System parameters identified for each of the plurality of states from the plurality of perturbations and measurements. A sliding mode control law may be defined for each of the plurality of states from the system parameters. A sliding mode control system for controlling the boost pressure of a turbocharger is also provided. An actuator for controlling the boost pressure is electrically controlled by a sliding mode controller to stabilize the turbocharger system toward a setpoint on a sliding surface. The control law is determined from a plurality of linear models from a plurality of operating states of the turbocharger system.

Methods and apparatus for performing computer-implemented personalized recommendations are disclosed. User information pertaining to a plurality of features of a plurality of users may be obtained. In addition, item information pertaining to a plurality of features of the plurality of items may be obtained. A plurality of sets of coefficients of a linear model may be obtained based at least in part on the user information and / or the item information such that each of the plurality of sets of coefficients corresponds to a different one of a plurality of items, where each of the plurality of sets of coefficients includes a plurality of coefficients, each of the plurality of coefficients corresponding to one of the plurality of features. In addition, at least one of the plurality of coefficients may be shared among the plurality of sets of coefficients for the plurality of items. Each of a plurality of scores for a user may be calculated using the linear model based at least in part upon a corresponding one of the plurality of sets of coefficients associated with a corresponding one of the plurality of items, where each of the plurality of scores indicates a level of interest in a corresponding one of a plurality of items. A plurality of confidence intervals may be ascertained, each of the plurality of confidence intervals indicating a range representing a level of confidence in a corresponding one of the plurality of scores associated with a corresponding one of the plurality of items. One of the plurality of items for which a sum of a corresponding one of the plurality of scores and a corresponding one of the plurality of confidence intervals is highest may be recommended.

A method and apparatus for chroma intra prediction based on reconstructed luma pixels and chroma pixels are disclosed. The chroma intra prediction is based on a linear model of derived co-located current luma pixels of the current luma block scaled by a scaling factor. The scaling factor comprises a product term of a division factor and a scaled covariance-like value associated with neighboring reconstructed luma and chroma pixels of a current block. The division factor is related to a first data range divided with rounding by a scaled variance-like value associated with the neighboring reconstructed luma pixels of the current block. The scaled covariance-like value, the first data range, or both of the scaled covariance-like value and the first data range are dependent on the internal bit depth, with which the chroma signal is processed during video coding process, according to an embodiment of the present invention.

The detection of electromechanical oscillations in power systems and the estimation of their parameters (frequency ƒ and damping ξ) is based on a linear time-varying model. The parameters of the linear model are on-line adapted by means of Kalman filtering techniques to approximate the measured signal y (representing the behaviour of the power system) optimally in the sense of a quadratic criterion. The estimated model parameters are then the basis for the calculation of parameters of the oscillations. Adaptive algorithms are based on a recursive calculation of the estimated parameter vector for each time-step based on the new value of the measured signal and the old values of the estimated parameters. As opposed to the collection of data over a time window and then performing the parameter identification at once, any change in the system can thus be detected much faster.

A human joint kinematics information extraction method includes generating a joint kinematics parameter estimator of a multiple linear model based on electromyogram (EMG) signals and joint kinematics information in the event of joint movement, measuring EMG signals in real time, and estimating joint kinematics information by applying the EMG signals measured in real time to the joint kinematics parameter estimator. Accordingly, human joint kinematics information may be extracted safely and accurately using surface EMG signals extracted non-invasively.

The invention relates to a stay cable stay force measuring method based on a vibration method using a linear model which belongs to the technical field of construction engineering. The method provided in the invention aims to solve problems of prior art. The problems are illustrated as follows. In the prior art, a stay force measuring method, normally based on the assumption of a specific physical model, is difficult to realize a unified recognition of various stay forces and in different measuring methods, the definition of parameters also varies. It is also difficult to operate to mix different measuring methods for use. All these factors undermine measuring accuracy. The method of the invention is carried out as follows. Tensioning a stay cable with two grades of pulling forces; under each grade of pulling force, using a speed accelerator sensor to measure the speed acceleration signals under the action of environment excitation or people excitation; converting the signals into auto power spectrum patterns; substituting each grade frequency of the two grades of pulling forces and the tensioning force into a linear model; identifying linear model coefficients corresponding to each grade frequency and realize the recognition of model parameters. The recognized linear model and the natural vibration frequency of each grade make it possible to recognize the stay cable stay force. With the method provided in the invention, a relatively-high-accuracy measuring value of stay force can be achieved and the problem of recognizing physical parameters to measure stay force using a traditional vibration method can be avoided, thus making the method reduce the uncertainty of a stay force test.

The invention provides a chrominance component prediction method in a hybrid video coding standard, and belongs to the field of video encoding. The purpose of the invention is to effectively eliminate the correlation between a chrominance component, a luminance component and another chrominance component in videos, and provides the chrominance component prediction method in the hybrid video coding standard, so as to further improve the performance of video coding. The chrominance component prediction method comprises the following steps: when performing chrominance component prediction through a linear model, using pixel points reconstructed around a current coding block to calculate parameters of the linear model; when performing parameter calculation, increasing the surrounding effective pixel points and removing the surrounding unusable pixel points; predicting a Cb chrominance component according to the obtained parameters through a reconstructed luminance component Y; and predicting a Cr chrominance component through the reconstructed luminance component Y and the reconstructed chrominance component Cb. According to the chrominance component prediction method in the hybrid video coding standard provided by the invention, model parameters are calculated through the effective pixel points surrounding the block, and the chrominance component Cr is predicted adaptively through the luminance component Y and the chrominance component Cb, so that the code efficiency is further improved.

The invention discloses a pedestrian re-recognition method based on human body symmetry and structural characteristic sparse expression in a monitoring video. In the method, the multi-frame image of a pedestrian to be identified is used for training, first of all, pedestrian contour detection and pedestrian contour extraction are carried out, then according to the human body symmetry, a pedestrian contour is divided into five areas of interest including a head portion, a left trunk, a right trunk, a left leg and a right leg, through selecting proper characteristics, feature extraction is carried out on the five areas of interest for inputting in a linearity model, and then a result to be re-recognized can be obtained. The method provided by the invention is highly relevant to actual situations and can effectively solve the problem of low pedestrian re-recognition accuracy in a video monitoring network.