40 results about "Structural risk" patented technology

The present invention generally relates to financial data processing, and in particular it relates to credit scoring, consumer profiling, consumer behavior analysis and modeling. More specifically, it relates to risk modeling using the inputs of credit bureau data, size of wallet data, and, optionally, internal data.

The present invention generally relates to financial data processing, and in particular it relates to credit scoring, consumer profiling, consumer behavior analysis and modeling. More specifically, it relates to risk modeling using the inputs of credit bureau data, size of wallet data, and, optionally, internal data.

The present invention generally relates to financial data processing, and in particular it relates to credit scoring, consumer profiling, consumer behavior analysis and modeling. More specifically, it relates to risk modeling using the inputs of credit bureau data, size of wallet data, and, optionally, internal data.

The present invention generally relates to financial data processing, and in particular it relates to credit scoring, consumer profiling, consumer behavior analysis and modeling. More specifically, it relates to risk modeling using the inputs of credit bureau data, size of wallet data, and, optionally, internal data.

The present invention generally relates to financial data processing, and in particular it relates to credit scoring, consumer profiling, consumer behavior analysis and modeling. More specifically, it relates to risk modeling using the inputs of credit bureau data, size of wallet data, and, optionally, internal data.

The present invention generally relates to financial data processing, and in particular it relates to credit scoring, consumer profiling, consumer behavior analysis and modeling. More specifically, it relates to risk modeling using the inputs of credit bureau data, size of wallet data, and, optionally, internal data.

The present invention generally relates to financial data processing, and in particular it relates to credit scoring, consumer profiling, consumer behavior analysis and modeling. More specifically, it relates to risk modeling using the inputs of credit bureau data, size of wallet data, and, optionally, internal data.

A method and system for calculating pipeline integrity business risk score for a pipeline network is provided. The method includes a step of first calculating a structural risk score, an operational risk score and a commercial risk score for each pipeline segment in a pipeline network. The method further includes calculating pipeline integrity business risk score for each pipeline segment. The structural risk score, operational risk score, commercial risk score and pipeline integrity business risk score for each pipeline segment is rolled-up to calculate the respective risk scores of a pipeline network. The rolled-up risk scores are calculated by computing weight factors for each pipeline segment, relative risk scores weight of each pipeline segment and relative risk scores contribution of each pipeline segment. The system of the invention comprises executable files, dynamic linked libraries and risk score computing modules configured to display the risk scores using a dashboard.

The invention relates to a b ridge seismic analyzing method based on seismic risk assessment, a spectral acceleration Sa in a ground motion intensity index is mainly adopted, Pup (Imi) is guided to be a seismic damage probability when the intensity of a bridge structure is IM, [delta][lambda] H,i a is seismic risk contribution parameter under seismic fortification levels, the Pup (Imi) and the [delta][lambda] H,I are utilized to calculate a seismic risk value [lambda] UP of a bridge structure, and anti-seismic property of the bridge structure is assessed. According to the invention, seismic risk values of dangerous components of the bridge are solved through the method provided by the invention, according to structural risk assessment relevant regulation, the anti-seismic properties of components of the bridge are researched, comparative study can be performed on seismic risk values of the components under different bridge seismic mitigation and isolation measures, and feasibility and applicability of different bridge seismic mitigation and isolation measures are assessed.

The invention relates to an improved extreme learning machine, particularly relates to an improved extreme learning machine combining learning thought of a least square vector machine, and belongs to the technical field of the artificial intelligence. On the basis of minimizing empirical risk of the traditional extreme learning machine, the improved extreme learning machine provided by the invention is combined with the learning thought of the least square vector machine, and is additionally provided with a structural risk control item, and calculates the result through efficiently adjusting the ratio of two kinds of risks, so that the risk of the overfitting generated by a module is greatly lowered. Three experiments of practically applying the method in the Sinc data set, the Boston Housing data set and the oxygen dissolving forecasting in the aquaculture indicate that compared with the ELM algorithm and the EOS-ELM algorithm, the forecast error of the method is relatively approximate with the training error, so that the problem of the overfitting is efficiently lowered, and the forecast precision is enhanced to a certain extent.

The invention provides a helicopter moving part service life management method based on digital twinning. The helicopter moving part service life management method comprises the steps of load data collection, structural stress analysis, crack parametric modeling, fracture mechanics simulation, agent model construction of fracture mechanics parameter prediction, probabilistic prediction of crack propagation, structural risk evaluation and dynamic adjustment of a maintenance inspection plan. The invention relates to an electronic device and a storage medium, which are used for executing a helicopter moving part life management method based on digital twinning. According to the invention, multiple scales of the structure are considered and multiple models are fused; and establishing a digitaltwinning body of the typical moving part structure of the helicopter, performing high-performance real-time simulation according to the flight parameters of the helicopter and the load and damage data of the key part of the structure, dynamically updating the damage state of the typical moving part of the helicopter, and adjusting the residual life and the maintenance and inspection plan of the structure.

The invention provides a photo-etching line width intelligence forecasting method based on a dimension-reduction and quantity-increment-type extreme learning machine, belongs to the field of automatic control, information technology and advanced manufacturing, and particularly relates to a method that special to the characteristic that training data are high in dimensionality and arrives in a batching mode in the modeling process of a photo-etching line width index, intelligent online forecasting of the photo-etching line width index is achieved by conducting matrix inversion and dimension reduction to a batching extreme learning machine based on minimizing of structural risks. The photo-etching line width intelligence forecasting method based on the dimension-reduction and quantity-increment-type extreme learning machine is characterized by comprising the following steps: matrix inversion in the batching extreme learning machine based on the minimizing of structural risks is conducted dimension reduction by adopting a matrix inversion dimension reduction formula to build the relations between model parameters of the extreme learning machine and new-arriving data, and online increment-quantity-type leaning and updating of output layer weights of the model parameters of the extreme learning machine are achieved. The index forecasting method which is used for the dimension-reduction and quantity-increment-type extreme learning machine and based on matrix inversion dimension reduction has good forecasting effect.

The invention relates to a method for extracting and recognizing facial features. The invention refers to a method for extracting and recognizing a two-dimensional front facial image. The specific recognizing methods are as follows: (1) a method based on a feature face: projecting an original image to a feature space, obtaining a series of dimension-reducing images and taking a main element thereof to express a face; (2) a method based on a neural network: utilizing a system formed by linking a great mass of simple processing units; (3), a method based on a support vector machine: building an ability of precisely studying a specific training sample and correctly recognizing any samples on the basis of vapnik-chervonenkis (VC) dimensional theory of the statistical learning theory and the minimum principle of the structural risk. The method for extracting and recognizing facial features can effectively reduce the error rate of the feature face part analysis by adopting the method based on the feature face; the method based on the neutral network can get good effect by applying to the face detection and the front face recognition; and therefore, the problem of complex mode recognition and action control can be solved.

The system evaluates a set of incentives based upon a variety of factors and/or predetermined rules and consumers are provided incentives when they satisfy one or more criteria of making payments for their transaction accounts. The one or more criteria include making an early payment, paying more than a minimum amount due, and making the payment through an automatic payment scheme. If a received payment satisfies one or more predefined criteria, one or more incentives are selected for the consumer, tracked, and provided to the consumer. The system analyzes payment information to determine attributes and positive (desirable) behavior and provides incentives to the consumer based on such positive behaviors.

The invention relates to a vortex structure recognition method based on Support Vector Machine (SVM), in aspect of feature extraction, statistics of coefficient matrix from wavelet transform of refractive index filed data is extracted, compared with traditional method of utilizing coefficient matrix from wavelet transform directly, the method winkles mass of redundant information, and decreases calculating amount. In aspect of classifier design, classification method based on support vector machine is brought forward, minimum structural risk principle is utilized to ensure that the vortex structure is correctly detached by classification surface and the classification interval is made maximum, in theory the invention decreases mis-recognition rate compared with the traditional classification method based on minimum experiential risk principle. The invention can represent and distinguish turbulence vortex structure more accurately; the invention establishes bases of optics effect accuracy for airborne optical equipment of missiles and airplanes, etc.

The invention provides a method for establishing a transmission grid safety risk comprehensive evaluation index system. The method comprises the steps of establishing an index system hierarchical structure which sequentially comprises a target layer, a criterion layer and an index layer; dividing indexes in the target layer into a transmission grid safety risk index and a transmission grid social benefits index; establishing evaluation indexes for the transmission grid safety risk index, which comprise a transmission and distribution equipment risk event index, a transmission grid structural risk index, a transmission power supply risk index and an extreme event risk index; establishing a clean utilization energy index and a continuous power supply social benefits index; establishing evaluation indexes respectively for the transmission and distribution equipment risk index, the transmission grid structural risk index, the transmission power supply risk index and the extreme event risk index, and calculating the transmission grid safety risk index; establishing evaluation indexes respectively for the clean utilization energy index and the continuous power supply social benefits index, and calculating the transmission grid social benefits index; and calculating a transmission grid safety risk comprehensive evaluation index.

The invention discloses an instruction out-of-order execution method, a processor and electronic equipment. The method comprises steps of allocating an index tag to the received instruction through the scoreboard, wherein each instruction has a specific tag; preferentially sending the instructions without structural risks and read-write risks to an arithmetic unit for execution, sending an operation result to a reorder cache, and after a delivery condition is met, delivering the result to a target register according to an index tag sequence to realize out-of-order execution and in-order delivery of the instructions. According to the method and the device, the scoreboard is matched with the reordering cache, so that write-read risks and write risks in data risks are eliminated, the application scene of out-of-order execution is expanded, the utilization rate of an arithmetic unit is increased, the overall speed of instruction execution is increased by reducing the waiting time of instruction execution, and the hardware cost is reduced.

The invention provides a method and a system for verifying the predicted risk of a liner. In view of the defect of the existing liner design by structural engineers, reliability engineers should evaluate the structural risk of liners before drop test of the liners. The fragile parts of a liner can be pointed out accurately before drop test, and the accuracy of prediction can be verified through test. Thus, the defects of products can be exposed more objectively through test, and packing operation can be improved. The quality of products is ensured. It is ensured that there will not be a large number of defective products after batch production.

The invention discloses an effectively classified information processing system. The system includes a fitting module used for conducting linear regression fitting on data to generate a fitting function, an empirical module used for obtaining empirical risks according to the fitting function and the data, a regularization module used for setting regularization items, a structure module used for calculating the sum of the regularization items and the empirical risks to obtain a structural risk, and an amendment module used for amending the fitting function according to the structural risk. Theregularization items adopt a multi-dimensional function. The fitting function adopts a high-order polynomial. The term number of the high-order polynomial is at least five. The amendment module worksout an L1 norm of the structural risk and conducts amendment on the fitting function according to the L1 norm. According to the effectively classified information processing system, by introducing theregularization items and avoiding items with small influence factors, inhomogeneous similar items cannot be classified into one class, and therefore the precision of an analysis result is improved.