Patents
Literature
Eureka-AI is an intelligent assistant for R&D personnel, combined with Patent DNA, to facilitate innovative research.
Eureka AI

12657 results about "Prediction methods" patented technology

Prediction Methods Summary. A technique performed on a database either to predict the response variable value based on a predictor variable or to study the relationship between the response variable and the predictor variables.

Automatic playback overshoot correction system

An automatic playback overshoot correction system predicts the position in the program material where the user expects to be when the user stops the fast forward or reverse progression of the program material. The invention determines the position where the program material was stopped. The media controller transitions to the new mode that the user selected, starting at the stopped position with an overshoot correction factor added or subtracted from it. The invention adapts to the user by remembering how much the user corrects after he stops the fast forward or reverse mode. Correction factors are calculated using the user's corrections and adjusting the correction factors if the user continues to make corrections. The invention also uses a prediction method to correctly place the user within the program upon transition out of either mode and determines if the speed of the fast forward or reverse modes and then automatically subtracts or adds, respectively, a time multiple to the frame where the transition was detected and positions the user at the correct frame. The time multiple is fine tuned if the user is consistently correcting after the fast forward or rewind mode stops. Another method initially tests the user's reaction time using a test video and asks the user to press the fast forward or reverse button on his control device during the test video and then asks the user to position the video to the place that he expected the system to have been. This time span is then used whenever the user uses the fast forward or reverse modes and is adjusted with a multiple for each speed. A final method allows the user to simply set a sensitivity setting that the system will use as a correction factor and a multiple is subtracted or added to the release frame whenever the user uses the fast forward or reverse modes, respectively.
Owner:TIVO SOLUTIONS INC

Attack-oriented network security situation prediction method, device and system

ActiveCN108494810ARealize dynamic associationIn line with the actual environmentData switching networksSecuring communicationCountermeasureAttack graph
The invention belongs to the technical field of network security and particularly relates to an attack-oriented network security situation prediction method, device and system. The method comprises the following steps: detecting and collecting alarm data and network environment operation and maintenance information in a network countermeasure environment, obtaining an element set required by network security situation prediction, wherein the element set comprises three types of information of an attacker, a defense party and a network environment; evaluating the attacker capability and the level of the defense party, establishing a dynamic Bayesian attack graph, and calculating an attack phase number and an attack state occurrence probability vector; and combining a vulnerability scoring standard and network asset information, and performing time-space dimension quantification on the network security situation value. According to the method, dynamic association of the situation elements of the defense party, the attacker, the environment information and the like is achieved, the actual environment of the network is better conformed to, the future situation and the attack occurrencetime can be accurately predicted, higher prediction efficiency is achieved, and storage scale and timeliness of network security situation awareness are optimized, so as to provide more effective guidance for network protection.
Owner:PLA STRATEGIC SUPPORT FORCE INFORMATION ENG UNIV PLA SSF IEU

Compact sandstone reservoir complex netted fracture prediction method

The invention belongs to the petroleum exploration field, and concretely relates to a compact sandstone reservoir complex netted fracture prediction method. The method comprises the steps of: building a geological structure model and a fracture growth model; testing magnitudes and directions of ancient and modern crustal stresses; completing a rock mechanic parameter experiment; testing rock mechanic parameters and fracture stress sensitivities; developing a fracture rock multistage composite rupture criterion; performing a rock deformation physical test to obtain a peak value intensity; building a relation model between single axle state stress-strain and fracture bulk density; building a relation model between triaxial state stress-strain and fracture bulk density and occurrence; building a relation model between single axle state stress-strain and fracture bulk density; calculating and stimulating fracture parameters under modern conditions; and verifying the reliability of a fracture quantitative prediction result. The method can accurately obtain compact sandstone reservoir complex netted fracture parameters, and perform quantitative characterization, is suitable for quantitative prediction of any fracture mainly with a brittle reservoir, and reduces exploitation risks and costs.
Owner:CHINA UNIV OF PETROLEUM (EAST CHINA)

Data-driven lithium ion battery cycle life prediction method based on AR (Autoregressive) model and RPF (Regularized Particle Filtering) algorithm

A data-driven lithium ion battery cycle life prediction method based on an AR (Autoregressive) model and an RPF (Regularized Particle Filtering) algorithm relates to a lithium ion battery cycle life prediction method and belongs to the technical field of data prediction. The invention solves the problems in the existing lithium ion battery cycle life prediction method that the model-based prediction method is complicated in modeling, and parameters are difficult to identify. The data-driven lithium ion battery cycle life prediction method combines time sequence analysis with particle filter method and comprises the following steps: the AR model is firstly utilized to realize the multi-step prediction on battery performance degradation process time sequence data; and then, aiming at the problem of uncertainty expression of the cycle life prediction result, the regularized particle filtering method is introduced, and a lithium ion battery cycle life prediction method framework is proposed. The method proposed by the invention can be used for effectively predicating the cycle life of a lithium ion battery and realizes the output of probability density distribution of the predication result, has good computational efficiency and uncertainty expression ability.
Owner:HARBIN INST OF TECH

Deep learning-based short-term traffic flow prediction method

The present invention discloses a deep learning method-based short-term traffic flow prediction method. The influence of the traffic flow rate change of the neighbor points of a prediction point, the time characteristic of the prediction point and the influence of the periodic characteristic of the prediction point on the traffic flow rate of the prediction point are considered simultaneously. According to the deep learning method-based short-term traffic flow prediction method of the invention, a convolutional neural network and a long and short-term memory (LSTM) recurrent neural network are combined to construct a Conv-LSTM deep neural network model; a two-way LSTM model is used to analyze the traffic flow historical data of the point and extract the periodic characteristic of the point; and a traffic flow trend and a periodic characteristic which are obtained through analysis are fused, so that the prediction of traffic flow can be realized. With the method of the invention adopted, the defect of the incapability of an existing method to make full use of time and space characteristics can be eliminated, the time and space characteristics of the traffic flow are fully extracted, and the periodic characteristic of the data of the traffic flow is fused with the time and space characteristics, and therefore, the accuracy of short-term traffic flow prediction results can be improved.
Owner:FUZHOU UNIV

A CNN and LSTM-based rolling bearing residual service life prediction method

The invention discloses a CNN and LSTM-based rolling bearing residual service life prediction method, and relates to the field of rolling bearing life prediction. The method aims to solve the problemthat residual service life (RUL) prediction of a rolling bearing is difficult in two modes of performance degradation gradual change faults and sudden faults. The method comprises the following stepsof: firstly, carrying out FFT (Fast Fourier Transform) on an original vibration signal of the rolling bearing, then carrying out normalization processing on a frequency domain amplitude signal obtained by preprocessing, and taking the frequency domain amplitude signal as the input of a CNN (Convolutional Neural Network); The CNN is used for automatically extracting data local abstract informationto mine deep features, and the problem that a traditional feature extraction method depends too much on expert experience is avoided. the deep features are input into an LSTM network, a trend quantitative health index is constructed, and a failure threshold value is determined at the same time; And finally, smoothing processing is carried out by using a moving average method, eliminating local oscillation, and a future failure moment is predicted by using polynomial curve fitting to realize rolling bearing RUL prediction. And the prediction result can be well close to the real life value.
Owner:HARBIN UNIV OF SCI & TECH
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products