390 results about "Quantitative model" patented technology

A system for efficient delivery of radio frequency (RF) energy to cardiac tissue with an ablation catheter used in catheter ablation, with new concepts regarding the interaction between RF energy and biological tissue. In addition, new insights into methods for coagulum reduction during RF ablation will be presented, and a quantitative model for ascertaining the propensity for coagulum formation during RF ablation will be introduced. Effective practical techniques a represented for multichannel simultaneous RF energy delivery with real-time calculation of the Coagulum Index, which estimates the probability of coagulum formation. This information is used in a feedback and control algorithm which effectively reduces the probability of coagulum formation during ablation. For each ablation channel, electrical coupling delivers an RF electrical current through an ablation electrode of the ablation catheter and a temperature sensor is positioned relative to the ablation electrode for measuring the temperature of cardiac tissue in contact with the ablation electrode. A current sensor is provided within each channel circuitry for measuring the current delivered through said electrical coupling and an information processor and RF output controller coupled to said temperature sensor and said current sensor for estimating the likelihood of coagulum formation. When this functionality is propagated simultaneously through multiple ablation channels, the resulting linear or curvilinear lesion is deeper with less gaps. Hence, the clinical result is improved due to improved lesion integrity.

A quantitative model combines a one-dimensional risk-assessment approach with expert knowledge to enable calculation of a probability or likelihood of exploitation of a threat to an information system asset without referring to actuarial information. A numerical value is established for one or more threats of attack on the information system asset based on expert knowledge without reference to actuarial data, and likewise, based on expert knowledge without reference to actuarial data, a numerical value is established for each of one or more access and privilege components of one or more vulnerabilities to attack on the information system asset. A security risk level for the information system asset is computed based upon the numerical values for threat and the access and privilege components for vulnerability so established.

The present invention is in the field of modeling and quantifying Regulatory Capital, Key Risk Indicators, Probability of Default, Exposure at Default, Loss Given Default, Liquidity Ratios, and Value at Risk, using quantitative models, Monte Carlo risk simulations, credit models, and business statistics, and relates to the modeling and analysis of Asset Liability Management, Credit Risk, Market Risk, Operational Risk, and Liquidity Risk for banks or financial institutions, allowing these firms to properly identify, assess, quantify, value, diversify, hedge, and generate periodic regulatory reports for supervisory authorities and Central Banks on their credit, market, and operational risk areas.

A response-time-difference calculation / request-number converting means (523) calculates a change of throughput based on a response time monitoring result, a target value of the response time, a quantitative model and performance specification information. A server selection means (524) input the performance specification information in the quantitative model acquired to calculate a throughput of each of pool servers, selects a pool server having a throughput corresponding to a throughput larger than and closest to the change of throughput, and instructs provisioning means (513) to execute configuration modification control on the selected pool server (502). The provisioning means (513) modifies the configuration so that the pool server (502) functions as an application server.

A method and system for selecting potential bidders or suppliers for a current electronic auction by using quantitative models to create a prioritized list of potential suppliers. A prioritization software analyzes a supplier's bidding participation history and bidding competitiveness history from the bidding data collected for all prior auctions in which the supplier participated or was requested by the auction coordinator to participate. Using the prior bid data, the software generates a participation score and a competitiveness score for the supplier. The participation and competitiveness scores may be combined to generate a combined score. All potential suppliers may be prioritized based on any of these scores individually or a combination of two or more of these scores. The prioritization list generated using quantitative values instead of subjective values minimizes the amount of unnecessary calls to potential suppliers and maximizes the likelihood that a called supplier will place a competitive bid in the current auction, thereby increasing the bidding yield per supplier selected.

The invention relates to an evaluation method for distribution network reliability, comprising the following steps: (1) transferring evaluation of the distribution network reliability to analysis on the distribution network blackout power factors, and carrying out decoupling treatment on the distribution network blackout power factors; (2) using an analytic hierarchy process (AHP) to establish an evaluation system of distribution network reliability; (3) establishing a comprehensive quantitative model of the distribution network reliability; (4) formulating an evaluation standard of the distribution network reliability; and (5) specifying weight indicators of the distribution network reliability. In the method, the evaluation theory of the distribution network reliability is combined with an actual power grid, so that the evaluation of the distribution network reliability is transferred to the analysis on the distribution network blackout factors and the evaluation system, the evaluation model, the evaluation standards and the evaluation indicator weights of the distribution network reliability are established, thus enabling to reflect the existing reliability level of the distribution network from a plurality angles, thereby playing a huge role in works, such as related decisions, high-reliability power supply area distribution network plans and the like on evaluation of the distribution network reliability, formulation of high-reliability distribution network establishment principle and establishment direction, and the like.

The invention relates to a method for analysing mass spectral data obtained from a sample in GC×GC (2-dimensional) mass spectrometry, comprising: (a) comparing mass spectral data of an analyte with mass spectral data of candidate compounds of known structure in a data library; (b) identifying a plurality of candidate compounds from the library based on similarities of mass spectral data; (c) predicting, for each candidate compound, a value of at least one analytical property using a quantitative model based on a plurality of molecular descriptors; and (d) calculating a match score for each candidate compound based on the value predicted in step (c) and a measured value of the analytical property for the analyte.

The present invention provides a system and method for building and analyzing a quantitative model from qualitative tacit knowledge. In one aspect of the present invention, there is provided a system for creating and analyzing a model of a user's semantic knowledge. The semantic knowledge model is based on cause and effect relationships as defined by the user. In another aspect of the present invention, there is provided a system for creating and analyzing a model of a user's episodic knowledge. The episodic knowledge model is based on the user's past experiences, including recalled stimuli and responses. The semantic and episodic models are used to describe the users internal mental model.

Fracture- and stress-induced sonic anisotropy is distinguished using a combination of image and sonic logs. Borehole image and sonic logs are acquired via known techniques. Analysis of sonic data from monopole P- and S-waves, monopole Stoneley and cross-dipole shear sonic data in an anisotropic formation are used to estimate at least one compressional and two shear moduli, and the dipole fast shear direction. Fracture analysis of image logs enables determination of fracture types and geometrical properties. Geological and geomechanical analysis from image logs provide a priori discrimination of natural fractures and stress-induced fractures. A forward quantitative model of natural fracture- and stress-induced sonic anisotropy based on the knowledge of fracture properties interpreted from image logs allows the computation of the fast-shear azimuth and the difference in slowness between the fast- and slow-shear. The misfit between predicted and observed sonic measurements (i.e. fast-shear azimuth and slownesses) is then optimized in order to discriminate depth zones with an elastic medium as being influenced by the presence of open natural fractures, closed natural fractures and fractures induced by non-equal principal stress effects.

The present invention relates to a method for quantitatively analyzing non-metallic inclusion in steel. Said method is characterized by that it makes the metallurgical microscope and automatic objective table be connected with camera and computer, utilizes the camera to collect the inclusion image from metallurgical microscope and feed it into computer to make image processing so as to implement identification of inclusion and parameter measurement, then utilizing quantitative model to calculate the measurement result or make comparison analysis with standard map so as to obtain quantitative evaluated result. Said invention also provides the concrete steps of implementing said method.

The invention relates to a video image sharpness processing method in a fog and haze day and a device thereof. The method comprises the following steps: establishing a quantitative model between an atmospheric transmission parameter and an optical propagation medium parameter; obtaining the optical propagation medium parameter of a practical measurement site, and calculating an atmospheric quantitative transmission parameter by utilizing the quantitative model; carrying out downsampling processing to an original image which contains fog and haze to obtain a preprocessing template image, and extracting a dark-channel image; extracting the atmospheric optical value A of the original image I(x) which contains the fog and haze, and initializing a depth-of-field correction factor (Omega); optimizing A and Omega in a transmissivity graph of the I(x); estimating a template transmissivity graph t<model> of a preprocessing image, and carrying out upsampling to obtain a transmissivity graph t(x); and according to the t(x) and the I(x), calculating a sharpness processing result image. A fog and haze image sharpness parameter is subjected to closed loop correction through the quantitative model, an image sampling and multi-scale interpolation method is combined to avoid defects that parameters can not be accurately artificially defined and can not be regulated according to practical environment in a dark channel image defogging process, and a sharpness processing effect is improved.

The invention discloses a quantitative adulteration detection method for peanut oil based on multiple-source spectroscopic data fusion. The quantitative adulteration detection method comprises the following steps: oil sample preparation; spectrum acquisition: respectively acquiring Raman spectrograms and near-infrared spectrograms of all adulterated oil samples; spectroscopic data fusion: performing data level fusion on the preprocessed Raman spectrograms and the preprocessed near-infrared spectrograms to obtain a fusion spectrogram; quantitative adulteration model establishment: extracting characteristic wavelengths of the fusion spectrogram, and establishing a quantitative peanut oil sample adulteration model through a multivariate quantitative calibration method; model verification: analyzing samples to be detected. The detection method performs data fusion on the edible oil spectrograms of two spectrums, has good complementarity, can reflect the inner characteristic information of edible oil more comprehensively, and is quick, convenient, efficient, non-destructive, free from preprocessing, high in accuracy, and strong in applicability.

Disclosed herein are systems and methods for keeping records and managing allocation in advertising campaigns according to rational quantitative models. In one facet, various quantitative methods are presented to efficiently manage experimentation and reallocation of advertising resources among many opportunities, seeking the best available return on investment. In an additional facet, a number of automated tools are described that keep statistics and manipulate bids and active sets in large advertising campaigns. For instance, in one illustrative embodiment, a system is presented for calculating an estimate of the relationship between position and bid for ad sites on an ad service which defines position. In another exemplary embodiment, an ad-campaign management system is presented which includes a cost-side reporter, a revenue-side reporter, a Bayesian value generator, and a bid generator.

The invention relates to a method for estimating suspended matter concentration in turbid lake water based on a VIIRS sensor. The method comprises: based on lake field measured spectrum and substance concentration data, analyzing optical properties of water body and water quality dominant factors; screening wave bands which are sensitive to suspended matter concentration variation in the turbid lake water, using curve fitting to determine an optimal model of suspended matter concentration and ground measured spectrum (Rrs); using Rrc data which is removed with water vapor and ozone absorption and corrected by Rayleigh scattering to establish a suspended matter concentration quantitative model which is suitable to be used for the VIIRS; and finally, using the established model on a VIIRS image, to accurately obtain interannual and inter-monthly variation rules and spatial distribution of the suspended matter concentration of the turbid lake water. Using the method can realize long-term high-precision monitoring of suspended matter concentration of a lake, and the method is helpful to scientifically evaluate interannual suspended matter concentration variation and development trends thereof, and effectively evaluate ecological environment changes of lake water.

Fracture- and stress-induced sonic anisotropy is distinguished using a combination of image and sonic logs. Borehole image and sonic logs are acquired via known techniques. Analysis of sonic data from monopole P- and S-waves, monopole Stoneley and cross-dipole shear sonic data in an anisotropic formation are used to estimate at least one compressional and two shear moduli, and the dipole fast shear direction. Fracture analysis of image logs enables determination of fracture types and geometrical properties. Geological and geomechanical analysis from image logs provide a priori discrimination of natural fractures and stress-induced fractures. A forward quantitative model of natural fracture- and stress-induced sonic anisotropy based on the knowledge of fracture properties interpreted from image logs allows the computation of the fast-shear azimuth and the difference in slowness between the fast- and slow-shear. The misfit between predicted and observed sonic measurements (i.e. fast-shear azimuth and slownesses) is then optimized in order to discriminate depth zones with an elastic medium as being influenced by the presence of open natural fractures, closed natural fractures and fractures induced by non-equal principal stress effects.

The invention relates to a determining method of amount of a shale oil deposit hydrofracture propping agent. The determining method mainly comprises the following steps of: (1) establishing a quantitative model of the relationship between a net jointing system and the amount of a propping agent based on an equivalent seepage principle; (2) preferably selecting quantity, volume and a permeability rate of a high-permeability zone by utilizing a universal Eclipse software for simulation of an oil deposit value based on reservoir liquid high-pressure property data, formation pressure data, rock data and a geologic model provided by a universal Petrel software for oil deposit geologic modeling; and (3) determining the amount of the propping agent for the shale oil deposit hydrofracture construction based on the model established in step (1) according to the quantity, the volume and the permeability rate of the high-permeability zone preferably selected in step (2). With the adoption of the determining method provided by the invention, shortcomings of the prior art can be overcome; blindness on the amount of the used sectional volume fracturing propping agent in a horizontal well of shale oil deposit can be effectively solved; fracturing and yield-increasing effects can be improved; and economic cost of fracturing can be saved.

The invention discloses a multi-view video code rate control method based on a key frame code rate-quantitative model. Code rate control is carried out at four layers including a view layer, a GOP layer, a frame layer and a macro block layer, and code rates among view points are allocated according to scale values of actual coding of the view points in the view layer; the total code rate of the whole GOP is allocated in the GOP layer and a coding quantization parameter of the key frame of each GOP is calculated according to the key frame code rate-quantitative model of the key frame, wherein the key frame code rate-quantitative model is obtained through analysis; code rates are allocated in the frame layer according to code rate allocation weights of B frame of different levels reasonably; and code rates of macro blocks are allocated in the macro block layer according to coding complexities of the macro blocks and coding quantization parameters of the macro blocks are solved. The multi-view video code rate control method has the advantages that the code rate-quantization model of the key frame is utilized, and an unequal code rate allocation strategy is adopted for the B frames of different levels, so that target bit allocation can be carried out more reasonably, code rate control is effectively carried out, and better rate-distortion performance and subjective quality are provided under the condition that code rate control precision is ensured.

The invention discloses a large-scale water pump unit and a working condition adjusting mode accurate quantitative model-selection method and belongs to the technical field of pump stations. The method is characterized by comprising the steps of A), firstly choosing various types of feasible pump sizes and number; B), determining the power of matching electrical motors in various feasible pump sizes initially; C), calculating variable working condition annual optimizing operation cost of a pump unit and total device cost caused when a pump station unit is provided with different working condition adjusting modes, the total cost of running and devices of the pump station unit in the period of the service life when a frequency conversion speed-changing adjusting mode, a total angle-changing adjusting mode and a half working condition adjusting mode are arranged for the pump station unit; and comparing total costs of different schemes and determining that the total cost which is the lowest is a best scheme. According to the pump unit device model-selection method, the usage requirement of a pump station can be guaranteed, high efficiency can be achieved, the cost of the device can be saved, and the running cost can be saved. The method can be applied to water pump units of national medium and large pump stations and selection of working condition adjusting modes and optimizing operation, and the electrical energy and the device cost are saved.

The invention relates to a prestack inversion method based on seismic restrained modeling. Prestack inversion results are directly determined by the quality of a constraint model, but a model for strong anisotropic carbonate reservoirs can not be quantitatively established through the facies-controlled modeling method or the karst modeling method or the reservoir facies modeling method or the like; in addition, follow-up inversion and reservoir prediction are not accurate due to errors existing in routine basic data of the methods. According to the prestack inversion method, based on prestack amplitude preservation gathering, firstly, the AVO effect is removed through the pure longitudinal wave extraction technology so as to improve data accuracy and then a quantitative model reflecting secondary reservoir distribution rules is established through relative impedance and logging data obtained through color inversion so as to instruct inversion. As is shown in actual data application, development details of secondary carbonate reservoirs can be depicted more clearly through prestack inversion results based on seismic restrained modeling. The prestack inversion method has the advantages that the prestack inversion method can be directly applied to actual data, and particularly development rules of reservoirs in deep zones without wells can be depicted more accurately through the prestack inversion method.

The present invention relates to a programmable device-based convolutional neural network acceleration method and system. The method comprises: designing a basic structure of a convolution neural network on the programmable device, and establishing the quantification model of the computing resource and the frequency about the parallelization parameter respectively; under different parallelization parameters, exploring the highest reachable clock frequency of the actual voltage, temperature, process deviation, establishing an analysis model of the actual highest reachable frequency and the parallelization parameter; and taking throughput calculation as an optimization purpose, according to the established quantitative model and analysis model, carrying out problem abstract on the convolutional neural network design space exploration, and using a certain search algorithm to solve the parallelization parameters with optimal performance. According to the method and system provided by the present invention, the delay margin reserved by the commercial design tools for the voltage, the temperature and the process deviation can be used while ensuring the stability and reliability of the accelerator, so that the performance of the convolutional neural network accelerator can be further improved.

The invention provides an online detection method for a double-effect concentration process of danhong injection. The method comprises the following steps: acquiring near infrared transmittance spectroscopy of danhong injection concentrate online by arranging a near infrared online detection device and collecting a concentrate sample; respectively measuring various pieces of quality control index information of the concentrate sample by a drying weighing method and high-performance liquid chromatography; removing abnormal spectrums; selecting a near infrared spectroscopy modeling waveband and a preprocessing method; establishing various quality control index quantitative models by a multivariate calibration algorithm; investigating the performance of the models by adopting various model evaluation indexes; online analyzing the variation trend of each quality control index in the concentration process by using the established models. According to the method, a near infrared online analysis technology is introduced into the concentration process of the danhong injection, so that the real-time monitoring of each quality control index (tanshinol, protocatechualdehyde, hydroxysafflor yellow A, rosmarinic acid, salvia acid B and moisture content) can be realized, the improvement on the quality control level in the danhong injection concentration process is facilitated, and the stable and reliable product quality is fully guaranteed.

The present invention relates to a system and a method of making optimal medical decisions. In one embodiment presented for illustration the system comprises a quantitative model of the disease in the form of transition probabilities, the quantitative model of the effect of the medical treatment (therapy, drug or remedy) on the course of the disease, the quantitative model of costs and benefits, including monetary as well as non-monetary costs and benefits, and the model of preferences with respect to the costs and benefits. Using probabilistic inference, distributions of parameters of models are extracted from the data and the opinions of parties involved in the medical treatment. An expectation of the value of the treatment is computed. Optimality of the treatment is achieved by choosing the treatment or its parameters that give the greatest value given the evidence, the models, and the preferences. Other embodiments are discussed.

The invention discloses a near infrared spectrum quantitative model simplification method based on principal component analysis. The method comprises the following steps: preprocessing a collected near infrared spectrum, and dividing the spectrum and corresponding to-be-detected component concentration data into a training set, a detection set and a prediction set; simultaneously combining a processed spectrum matrix and a to-be-detected component concentration matrix to form a simultaneous matrix, and decomposing principal components to obtain each wavelength variable and load of the to-be-detected component; calculating an included angle between each wavelength variable and the load of the to-be-detected component concentration; reserving the wavelength variable with the angular value meeting an optimal angular value; establishing an optimal model by utilizing the reserved wavelength variable, and predicting the to-be-detected component concentration of a sample of the prediction set. By adopting the method, the wavelength variable containing the concentration information can be accurately extracted, the quantitative analysis model can be maximally simplified while the precision is guaranteed, and a novel quantitative model simplification method is provided for the near infrared spectrum multivariate correction and analysis.

The invention provides a method for accomplishing stream media service, its system and network equipment, belonging to network communication technology field. The method comprises: first, establishing math-optimizing problem according to user experience quantitative model, solving to determine every parameter of user experience quantitative model; setting control strategy of stream media server according to every determined parameter; the stream media server reads the data file of stream media according to the control strategy, then transmits media stream to user terminal and provides stream media service for user. The inventive embodiment is irrelevant to concrete representation of user experience quantitative model, so the inventive embodiment provides a general realization frame for stream media service and optimum user experience for stream media.

The invention provides a qualitative and quantitative combined near infrared quantitative model construction method which comprises the following steps: obtaining an actual sample of a modeling correction set, and detecting a basic chemical component of the actual sample; scanning a spectrum corresponding to the correction sample, and culling an abnormal sample; qualitatively projecting an available spectrum; classifying projection data; forecasting a verification set consisting of a near infrared spectrum of each type and a chemical value through a modeling set, and calculating a forecasting error; randomly selecting near infrared wavelength points; calculating a general correction set error corresponding to each generation of the wavelength points; determining near infrared wavelength selection points and characteristic information of the infrared spectrums according to the minimum general correction set error; rebuilding a regression model through the spectrums and the chemical values of the correction set; detecting the chemical values of verification samples, obtaining corresponding spectrums, and quantitatively evaluating the regression model. According to the method, the spectrums of the correction set are qualitatively projected and analyzed, so that the method is adaptive to changes of the spectrums, and the forecasting stability of the model can be kept.

The invention discloses a quantitative remote sensing method of suspended sediment. The quantitative remote sensing method comprises the following steps: step S1, carrying out field water body spectral measurement and suspended sediment content measurement; step S2, carrying out equivalent remote sensing reflectivity calculation; step S3, randomly dividing collected water samples into a modeling sample and a verification sample; step S4, establishing a sediment remote sensing quantitative model by the modeling sample; step S5, acquiring a satellite image and pre-processing the satellite image; and step S6, carrying out inversion to obtain the content of the suspended sediment in a research region. By establishing the sediment remote sensing quantitative model, the sediment remote sensing quantitative model is applied to a satellite remote sensing image through ENVI (The Environment for Visualizing Images) software and spatial and temporal distribution of the sediment in the research region can be automatically drawn; and the method is a scientific and efficient river-mouth off-shore sediment research method.

The invention provides a global optimization method for control parameters of a photovoltaic power generation system. The method comprises the following steps: firstly, an established differential-algebraic equation of a photovoltaic plant power generation system is linearized at a steady-state value to obtain a state matrix used for analyzing the small disturbance stability of the system; then the influence on the system small disturbance stability of the system caused by control parameters is analyzed by calculating the eigenvalue, the damping ratio and participation factors of the state matrix to further determine the reasonable variation range of the control parameters; and finally, a quantitative model is introduced to serve as a parameter optimization objective function and the optimization algorithm is utilized to achieve global optimization of the control parameters of the photovoltaic plant power generation system. According to the invention, as for the small disturbance stability of the system, a scientific and reasonable quantification standard is established, and the problem of difficult single optimization due to mutual coupling of the system control parameters is solved. By adopting the method for parameter optimization, global optimization of the control parameters can be realized, and the small disturbance stability of the photovoltaic plant power generation system is improved.

The invention provides a method for distinguishing variety of fritillaria and detecting total alkaloid content of the fritillaria by virtue of near infrared spectrum. The method provided by the invention comprises the following steps: (1) collecting a fritillaria sample; (2) measuring the near infrared diffuse reflection spectrogram of the fritillaria sample, preprocessing the 4000-5000cm<-1> wave band in the spectrogram, and performing cluster analysis on the pre-processed near infrared spectrogram to build a qualitative model; or preprocessing the 4000-7000cm<-1> wave band in the spectrogram, so as to obtain an absorbance, associating the absorbance with the alkaloid content of the sample measured by virtue of bromothymol blue colorimetry, and building a quantitative correction model for detecting alkaloid by one or more methods of a partial least squares method, a principal component regression method and a multiple linear regression method; (3) collecting the near infrared spectrogram of the sample to be measured, after the corresponding preprocessing is performed, distinguishing the variety of the fritillaria and detecting total alkaloid content of the fritillaria by utilizing the built qualitative model or quantitative correction model. The method provided by the invention has the characteristics of fast speed, no damage, environment friendliness and low cost.

The invention discloses a near infrared spectroscopy detection method of a radix curcumae medicinal material. The method comprises near infrared detection of a water content, and / or, curdione, germacrone, and cincumol in radix curcumae. The method comprises the following steps: (1) preparation of a medicinal material sample, in which, a medicinal material is pulverized and sieved for later use; (2) target component detection, which comprises water content detection (through a methylbenzene method) or detection of curdione, germacrone, and cincumol (through a liquid phase method); (3) collection of near infrared spectroscopic data; (4) establishment of a quantitative model, in which, the near infrared spectroscopic data is associated with a known component content and the water content to establish the calibration quantitative model; and (5) near infrared detection of an unknown to-be-detected sample. The radix curcumae medicinal material can be quickly detected through the method; the method has advantages of simple operation and high accuracy; and the material is not wasted. Safe, efficient, controllable, and stable quality of the radix curcumae medicinal material is ensured.