9457 results about "Non linearite" patented technology

A system and process that provides an on-line, interactive, and fully integrated benefit-driven value exchange and settlement program that monitors, evaluates, and manages economic and personal benefits and executes functions to produce and acquire the maximum or preferred benefit items for users by guiding and automating appropriate payment and settlement actions. The present invention finds useful patterns in data; produces conclusions based on rules and experience; responds to environmental changes with or without human intervention; and, may evolve through selecting the best results from random mutations all of which are intended to maximise user value.

The Quanton virtual machine approximates solutions to NP-Hard problems in factorial spaces in polynomial time. The data representation and methods emulate quantum computing on classical hardware but also implement quantum computing if run on quantum hardware. The Quanton uses permutations indexed by Lehmer codes and permutation-operators to represent quantum gates and operations. A generating function embeds the indexes into a geometric object for efficient compressed representation. A nonlinear directional probability distribution is embedded to the manifold and at the tangent space to each index point is also a linear probability distribution. Simple vector operations on the distributions correspond to quantum gate operations. The Quanton provides features of quantum computing: superpositioning, quantization and entanglement surrogates. Populations of Quantons are evolved as local evolving gate operations solving problems or as solution candidates in an Estimation of Distribution algorithm. The Quanton representation and methods are fully parallel on any hardware.

In a database, a frequently retrieved column is encrypted using a common key, and other columns are encrypted using a specific row key. Thus, a retrieving process can be performed at a high speed, and the security can be improved. Then, the row and column of the database are encrypted by assuming the plaintext to be encrypted as a bit string, and performing a binary operation with a random bit string. A random bit string is obtained by sequentially generating multidimensional vectors using a nonlinear function by defining a predetermined bit length as 1 word and a plurality of words as components of the multidimensional vector.

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 discloses an experimental method of simulating dynamic and static load and a device thereof, relating to indoor model experimental technique of geotechnical engineering. The structure of the device is as follows: a model experimental platform (1), a model experimental box (2) and a load transmitting lever (3) are sequentially connected; the load transmitting lever (3) is respectively connected with a static load system (4) and a dynamic load system (5) which are respectively connected with a dynamic signal testing system (6); the dynamic signal testing system (6), a data acquisition system (7) and a computer (8) are sequentially connected. The device caries out dynamic and static load experiments on different structure models, and can monitor and collect non-linear properties of deformation and stress of gneiss under complex load condition in real time. The device has intuitional testing principle, simple structure, high precision, good stability, easy operation, convenient detachment and no high technical requirement to mounting and testing workers.

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 become 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 system of non-linear feedback control for spinal cord stimulation is provided. The system comprises a lead adapted to be implanted within an epidural space of a dorsal column of a patients spine, and a pulse generator (PG) electrically coupled to the lead. The PG is configured to deliver spinal cord stimulation (SCS) therapy. The system also comprises a sensing circuitry configured to sense an evoked compound action potential (ECAP) response that propagates along the neural pathway. The system also comprises a processor programmed to operation, in response to instructions stored on a non-transient computer-readable medium, to obtain a baseline ECAP response when the lead and spinal cord tissue properties are in baseline states; analyze ECAP responses relative to the baseline ECAP response to obtain an ECAP feedback difference indicative of a change in at least one of the baseline state of the lead and the baseline state of the spinal cord tissue properties. The processor is also programmed to adjust an SCS therapy based on the ECAP feedback.

A classifier training method and apparatus for training, a linear classifier trained by the method, and its use, are disclosed. In training the linear classifier, signatures for a set of training samples, such as images, in the form of multi-dimension vectors in a first multi-dimensional space, are converted to a second multi-dimension space, of the same or higher dimensionality than the first multi-dimension space, by applying a set of embedding functions, one for each dimension of the vector space. A linear classifier is trained in the second multi-dimension space. The linear classifier can approximate the accuracy of a non-linear classifier in the original space when predicting labels for new samples, but with lower computation cost in the learning phase.

The invention discloses a text sentiment classification algorithm based on a convolutional neural network and an attention mechanism. The text sentiment classification algorithm comprises the steps of1, establishing the convolutional neural network comprising multiple convolutions and multiple kinds of pooling, and using sentiment classification text for training to obtain a first model; 2, establishing the multi-head point product attention mechanism into which residual connection and nonlinearity are added, and using the sentiment classification text for training to obtain a second model; 3, conducting model fusion on the two models to obtain sentiment classification of the text. Multiple granularity, the convolutions and multiple kinds of pooling are fused into the convolutional neuralnetwork, the residual connection and the nonlinearity are introduced into the attention mechanism, and attention is calculated several times to obtain two text sentiment classification models. Through a Bagging model fusion method, a fusion model is obtained, the text is classified, the advantages that the convolutional neural network can well capture local features and the attention mechanism can well capture global information can be combined, and the more comprehensive text sentiment classification models are obtained.

The invention discloses a microgrid running optimization system and method based on power and heat combined dispatching. Running restrains such as the output force characteristic, start, stop and climbing of a distributed type generator set and the charge and discharge characteristics of power and heat energy storage are comprehensively considered, a microgrid power and heat combined dispatching model containing a fan, a photovoltaic cell, a combined heat and power generation system, an electric boiler, a fuel cell and an energy storage (electrical energy storage and heat energy storage) is built, the 0-1 mixed integer nonlinear programming method is adopted for solving the optimal output force and total running cost of all units in the network, and power and heat dispatching contrastive analysis is carried out on the system and a traditional separated generation and combined generation optimization model. In addition, the influences of the heat and power reliability difference on the running are analyzed and discussed, and therefore the heat and power dispatching module is more reasonable, comprehensive and universal.

A synchronous quick calibration method of a plurality of video cameras in a three-dimensional scanning system, which includes: (1) setting a regular truncated rectangular pyramid calibration object, setting eight calibration balls at the vertexes of the truncated rectangular pyramid, and respectively setting two reference calibration balls at the upper and lower planes; (2) using the video cameras to pick-up the calibration object, adopting the two-threshold segmentation method to respectively obtain the corresponding circles of the upper and lower planes, extracting centers of the circles, obtaining three groups of corresponding relationships between circle center points in the image and the centres of calibration ball in the space, solving the homography matrix to obtain the internal parameter matrix and external parameter matrix and obtaining the distortion coefficient, taking the solved video camera parameter as the initial values, and then using a non-linear optimization method to obtain the optimum solution of a single video camera parameter; (3) obtaining in sequence the external parameter matrix between a plurality of video cameras and a certain video camera in the space, using the polar curve geometric constraint relationship of the binocular stereo vision to establish an optimizing object function, and then adopting a non-linear optimization method to solve to get the optimum solution of the external parameter matrix between two video cameras.

A circuit and method for controlling a switching regulator utilize a combination of variable off-time control (or frequency control) and variable peak current control to achieve high efficiency at a wide range of load conditions. A non-linear control circuit receives an error voltage and generates a first control signal for controlling a frequency control circuit and a second control signal for controlling a peak current control circuit. The frequency control circuit and the peak current control circuit operate in conjunction over the entire range of load conditions with the frequency control dominates at light load (or low power) conditions and the variable peak current control dominates at moderate to heavy load (or high power) conditions. The switching regulator transitions smoothly between frequency control and peak current control with continous loop gain throughout the transition region.

A Dynamic Digital Pre-Distortion (DDPD) system is disclosed to rapidly correct power amplifier (PA) non-linearity and memory effects. To perform pre-distortion, a DDPD engine predistorts an input signal in order to cancel PA nonlinearities as the signal is amplified by the PA. The DDPD engine is implemented as a composite of one linear filter and N-1 high order term linear filters. The bank of linear filters have programmable complex coefficients. To compute the coefficients, samples from the transmit path and a feedback path are captured, and covariance matrices A and B are computed using optimized hardware. After the covariance matrices are computed, Gaussian elimination processing may be employed to compute the coefficients. Mathematical and hardware optimizations may be employed to simplify and reduce the number of multiplication operands and other operations, which can enable the DDPD system to fit within a single chip.

A computer method and apparatus for adaptive model predictive control (MPC) of multivariable processes is disclosed. The adaptive MPC system can perform automatic implementation for a new MPC controller, and, for an existing MPC controller, it can perform automatic maintenance when necessary. The adaptive MPC system consists of three modules: an MPC control module, an online identification module and a control monitor module. In MPC implementation, the online identification module and the MPC control module work together and perform various steps in MPC implementation automatically and efficiently. When the MPC controller is online, the control monitor module continuously monitors the MPC performance and model quality. When control performance becomes poor and considerable model degradation is detected, monitor module will start the maintenance by activating the online identification module. The identification module will re-identify the model and replace the old model. For strongly nonlinear process units, multiple models are identified and used in control.

The invention discloses a convolutional neural network structure-based traffic flow prediction method. The method comprises the following steps of 1) establishing a traffic flow data set and preprocessing the data set: establishing the traffic flow data set according to obtained traffic flow data, preprocessing the data set, constructing a data set sample matrix, and dividing the data set into a training set and a test set; 2) establishing a single-layer conventional convolutional neural network, removing a pooling layer, constructing a feature extraction network of a road traffic flow matrix,adding a sigmoid nonlinear regression layer to a full connection layer, and constructing a road traffic flow nonlinear regression prediction network; and 3) training the convolutional neural networkand realizing real-time prediction of short-term traffic flow: defining a model objective function, taking the training set as an input of a convolutional neural network model, solving an optimal parameter of the model to finish model training, and performing real-time traffic flow prediction on the test set by utilizing the trained convolutional neural network model. The short-term prediction accuracy of the traffic flow is improved.

A system and method for the discovery and selection of an optimal probability model. Probability model selection can be formulated as an optimization problem with linear order constraints and a non-linear objective function. In one aspect, an algorithm that employs an information-theoretic based approach is used in which a scalable combinatoric search is utilized as defined by an initial solution and null vectors. The theoretical development of the algorithm has led to a property that can be interpreted semantically as the weight of evidence in information theory.

A method and apparatus are disclosed for generating a reduced nonlinear model, whose significant properties include accuracy, compact size, reliability and speed. These properties enable nonlinear real-time optimization and detailed analysis of any operation that can be modeled this way. An array of related applications is disclosed for providing a deep understanding of the behavior of the optimum of an operation. Such understanding includes the ability to directly visualize an objective function response surface and the detailed interrelationships between independent and dependent variables for optimal operation. Additional applications are disclosed for verbally expressing operating instructions based on optimality of an operation, and an accurate means to quantify the ongoing benefits due to nonlinear optimization.

A method and apparatus for propagating charge through a sensor and implementation thereof is provided. The method and apparatus may be used to inspect specimens, the sensor operating to advance an accumulated charge between gates of the TDI sensor. The design implementation provides a set of values representing a plurality of out of phase signals, such as sinusoidal or trapezoidal signals. These out of phase signals are converted and transmitted to the sensor. The converted signals cause the sensor to transfer charges in the sensor toward an end of the sensor. Aspects such as feed through correction and correction of nonlinearities are addressed.

A description is given of a BAW apparatus having a first BAW resonator and a second BAW resonator which are connected antiparallel to one another so as to reduce non-linear effects, in particular harmonics.

An improved forward-masking method of recording and processing neural responses (“NR”) is provided, wherein the method does not assume a linear system response and does not assume a linear response at the interface between electrodes and tissue. The method of the present invention cancels out non-linearities and/or system hysteresis. Other artifacts such as system cross-talk between stimulation and recording circuits are also canceled out.

A switching device includes a substrate; a first electrode formed over the substrate; a second electrode formed over the first electrode; a switching medium disposed between the first and second electrode; and a nonlinear element disposed between the first and second electrodes and electrically coupled in series to the first electrode and the switching medium. The nonlinear element is configured to change from a first resistance state to a second resistance state on application of a voltage greater than a threshold.

Nonlinear adaptive resource management systems and methods are provided. According to one embodiment, a controller identifies and prevents resource starvation in resource-limited systems. To function correctly, system processes require resources that can be exhausted when under high load conditions. If the load conditions continue a complete system failure may occur. Controllers functioning in accordance with embodiments of the present invention avoid these failures by distribution shaping that completely avoids undesirable states. According to one embodiment, a Markov Birth/Death Chain model of the resource usage is built based on the structure of the system, with the number of states determined by the amount of resources, and the transition probabilities by the instantaneous rates of observed consumption and release. A control stage is used to guide a controller that denies some resource requests in real systems in a principled manner, thereby reducing the demand rate and the resulting distribution of resource states.