154 results about "Uncertain data" patented technology

The invention provides a fuzzy evaluation method used for evaluating a network security situation value of a hierarchical model, and the method comprises the steps that: an evaluation factor set and a judgment set used for evaluating network security situation indexes of a top-level node of the hierarchical model are determined; a membership function is established, and the probability that attribute data of each child node in the evaluation factor set belong to different judgment ranks in the judgment set is determined according to the membership function; a fuzzy evaluation matrix is established on the basis of the determined probability that each child node belongs to different judgment ranks; the importance weight of each child node over the top-level node of the hierarchical model isdetermined; and the network security situation indexes of the top-level node is calculated according to the established fuzzy evaluation matrix and the determined importance weight of each child nodeover the top-level node of the hierarchical model, and thereby the network security situation value of the hierarchical model is finally calculated. The fuzzy evaluation method has the advantages that the security situation of a network is evaluated through introducing fuzzy mathematics and a fuzzy analytical hierarchy process (FAHP) into the fuzzy evaluation method, and thereby the problem of data uncertainty during the evaluation of the network security situation is well solved.

Routing method for computing routes over uncertain geo-spatial data whereby only upon visiting the geographic entities it can be determined whether the needed service or product is actually provided and is adequate. When dealing with uncertain data, the returned route may need to go via several entities of the same type. Another routing method consists of finding an efficient and effective route from a starting point to an ending point going trough a predetermined number of objects, wherein each object represents a geographical entity of a unique type, and each object is selected from a distinct set of similar objects.

The present invention comprises a system that incorporates the natural relationships of patient parameters into a medical monitoring system in order to increase monitoring specificity by reducing false positive alarms resulting from spurious data and inconclusive data. The invention also comprises a system that incorporates the natural relationships of patient parameters in order to use the data obtained from those parameters to non-invasively monitor a patient parameter that typically precludes direct monitoring.

The invention discloses an e-commerce platform individual recommendation method based on a weighted frequent item set mining algorithm. The method, for the browse behaviors of an e-commerce platform user, assigns different probability values to different items according to behaviors of click browsing, collecting, adding to shopping cart and purchasing so as to reflect the user's preference for different items, and in combination with the income of different items (ie, commodities), mines the weighted frequent item set in an e-commerce platform user browse data set to achieve effective individual recommendation. The invention provides a weight decision downward closure feature and a weighted frequent subset existence characteristic for the e-commerce platform user browse data set weighted frequent item set mining, and provides a uncertain data frequent item set mining algorithm based on weight decision downward closure feature according to the above two characteristics, and takes account of the user's preference for different items and the benefits that the items bring to a merchant, and improves the mining efficiency.

The invention relates to a maximal pattern mining method for uncertain data based on depth-first. The maximal pattern mining method comprises three major technical parts of uncertain data processing, frequent item set judgment and the maximal pattern mining method. The uncertain data processing refers to converting an uncertain data horizontal format of which the main key is an affair ID into an uncertain data vertical format of which the main key is an item ID by virtue of data vertical format conversion. The frequent item set judgment refers to the process of calculating whether the support degree of an item set is greater than or equal to a given support degree threshold and whether the confidence degree of the item set is greater than or equal to a given confidence degree threshold. The maximal pattern mining method is the process of mining the maximal frequent item set, and in the mining process, the converted vertical-format data is taken as the input, and all the uncertain data maximal pattern frequent item sets are mined out according to the given support degree and confidence degree thresholds. The maximal pattern mining method for the uncertain data based on depth-first is capable of effectively obtaining the value information in the uncertain data, and also has high mining efficiency.

The invention discloses a vector quantization based long-term intuitionistic fuzzy time series prediction method. The method includes the steps of A, intuitionistic fuzzification preprocessing of serial data and B, vector quantization based long-term intuitionistic fuzzy time series prediction. A long-term intuitionistic fuzzy time series prediction model built by the method expands single output of the serial data into multiple outputs, a predicated value is converted from a scalar into a vector, and accordingly long-term predication performance of a time series system is improved to a great extent. A sliding window mechanism is introduced into the method, fuzzy change characteristics of the serial data are acquired accurately and rapidly; a discourse domain internal is divided dynamically by the aid of an IFCM (intuitionistic fuzzy C-means) algorithm to be more close to the actual uncertain data distribution; by the aid of vector quantization based long-term time range prediction, the problems of zero matching of intuitionistic fuzzy rules and system error accumulation can be well solved; through example verification and result analysis, the model has good predication performance.

The invention discloses a method and system for detecting uncertain data stream-oriented complex events in manufacture internet of things; the method comprises: reading a target atomic event with probability value; during NFA (nondeterministic finite automaton) state transition, if a corresponding matching tree is present, using a present matching tree as a left node and the target atomic event as a right node to generate a matching tree using post-transition NFA state as a root; if a corresponding matching tree is not present, using NFA initial state as a left leaf node and the target atomic event as a right leaf node to generate a matching tree with post-transition NFA state as a root; if it is detected that a root node of the matching tree is NFA termination state and timestamp size of the matching tree is in sliding window time, giving priority to deep traversal of the right leaf node of the matching tree with the NFA termination state as a root node, and outputting complex events in a reverse sequence; uncertain data stream complex events are detected by using NFA in conjunction with the matching tree, and event detection efficiency is improved.

A method for finding a distance between a plurality of time series, wherein each individual time series in the plurality of time series includes data, wherein the data is uncertain data, the method comprising: selecting at least two time series from a plurality of time series; computing a first difference value between the two series at a given instant of time; mapping the first difference value with a table of values; computing a second difference value using the table of values, wherein the second distance value is a measure of similarity between the time series.

A robust optimization-based decision support tool for reservoir development planning can comprise a source of input data, an optimization model, a high fidelity model for simulating the reservoir, and one or more solution routines interfacing with the optimization model. The optimization model can consider unknown parameters having uncertainties directly within the optimization model. The model can systematically address uncertain data, for example, comprehensively or even taking all uncertain data into account. Accordingly, the optimization model can provide flexible or robust optimization solutions that remain feasible over an uncertainty space. Once the reservoir model is optimized, final development plans may be generated.