161 results about "Certainty factor" patented technology

A management system, which manages multiple monitoring target apparatuses, computes a certainty factor denoting the probability that a certain configuration change is the root cause of a performance failure that has occurred in a certain monitoring target apparatus based on rule information, computer system performance information, and a configuration change history, and displays management information from the standpoint of the configuration change based on the computation result.

The invention discloses a staged multi-path model algorithm of urban rail transit network passenger flow distribution. The algorithm comprises the following steps of: dividing definite factors and indefinite factors affecting the outgoing of passengers into two stages to calculate and correct the passenger flow distribution proportion of a path, determining the initial distribution proportion of the passenger flow on the path by using the comprehensive outgoing impedance of time unit as a main factor, and correcting the proportion by considering the transfer times and the crowdedness degree on the basis of time window constraints according to a generated full origin-destination (OD) passenger flow distribution path set. The model and the algorithm provided by the invention have higher reasonability and practicability and can effectively serve the passenger flow management of the urban rail transit, meet the service requirement of clearing automatic fare collection (AFC) passenger flow and fare, and provide auxiliary decision proof for operation organization optimization and coordination.

The invention discloses an establishing method of an uncertainty mid-term and long-term hydrological forecasting model, comprising the following steps: using a wavelet analysis (WA), an artificial neural network (ANN) and a hydrological frequency analysis (HFA) in combination to establish the uncertainty mid-term and long-term hydrological forecasting model; dividing the original sequence into two sections of a main sequence and a random sequence on the basis that WA is used to reveal multiple time dimension variation characteristics of the hydrological sequences, adopting ANN for analogue forecasting on the main sequence and hydrological frequency analysis on the random sequence and overlapping the results of the two sections to be a final forecasting value. The model is used for the mid-term and long-term hydrological forecasting in the Yellow River estuary area, and compared with the traditional method, the results show that the model can reveal the time and frequency structures and variation characteristics of the sequences, has high forecast value result precision and acceptance rate, can quantitatively analyze and describe the impact of hydrological uncertain factors on the forecasting result and can obtain the analogue forecasting value of different frequencies to corresponding hydrological sequences.

The invention discloses a robust tracking control method of a network control system based on the switching control theory. A mixed node driving mechanism of active variable sampling time and an event is established, and it is guaranteed that network-induced delay is always shorter than a sampling period; based on the mixed node driving mechanism of the active variable sampling time and the event, a tracking control model of the uncertain network control system is established; based on a rasterization processing method, the tracking control model of the network control system is converted into a switching system model with a limited switching rule, an H8robust tracking controller of a discrete network control system achieving the given disturbance inhibition level is designed, and finally communication limitation and tracking control of the network control system under the uncertain system condition are achieved. The method has a good inhibition effect on induced delay, packet loss and system uncertain factors of an uncertain network.

To search out a new word that should be newly registered in a dictionary contained in a segmentation device for segmenting a text into words. This system inputs a training text into the segmentation device to cause the segmentation device to segment the training text into words, and thereby generates a plurality of segmentation candidates in association with certainty factors of the results of the segmentation, the segmentation candidates respectively containing mutually different combinations of words as results of the segmentation of the training text. Then, this system computes a likelihood that the each word is a new word by summing up some of the certainty factors that are respectively associated with some of the plurality of segmentation candidates that contain the each word. Then, from among combinations of words each contained in at least any one of the segmentation candidates, the system searches combinations of words contained in at least any one of the segmentation candidates and containing words with which the entire training text can be written, in order to find out a combination that minimizes an information entropy of words assuming that each word belonging to the combinations appears in the training text at a frequency according to the likelihood corresponding to the word, and thereafter for outputting the found-out combination as the combination of words including the new word.

According to the present invention, there is provided a computer system, method and apparatus for making intelligent recommendations for dynamic content caching. In one embodiment of the present invention there is provided a computer implemented method for generating intelligent caching recommendations related to dynamic web content for use on a caching system. The method comprising extracting data associated with the dynamic content of interest in accordance with a predetermined data model. Next analyzing the extracted data in accordance with a plurality of certainty factors and a rule based expert system. Completing the analysis and generating a set of caching recommendations from the analyzed data suitable for use by the caching system. Implementing the recommendations in the caching system are repeated iteratively, as in a loop, automatically generating intelligent caching recommendations related to the dynamic web content for use on the caching system.

The invention discloses an energy scheduling method for a microgrid, which is characterized by including adopting the Monte Carlo method to conduct stochastic simulation on uncertainty factors in operation of the microgrid to generate samples formed by output power of a power supply with renewable energy sources and the like; providing the confidence level of load satisfying rate of the microgrid, setting rotating reserve probability constraint condition of the microgrid and forming a target function of microgrid energy scheduling in minimum mode through planning operation cost of energy scheduling time periods of the microgrid; designing energy scheduling algorithm combining the stochastic simulation in the Monte Carlo method and genetic algorithm to conduct solution of the target function of microgrid energy scheduling to obtain the optimum energy scheduling scheme of the microgrid. The energy scheduling method achieves balance between operation economical performance and operation reliability of the microgrid and meets actual operation requirements of the microgrid by setting the confidence level of the load satisfying rate of the microgrid, introducing the rotating reserve probability constraint condition of the microgrid and processing various uncertain factors in the microgrid.

The invention discloses a power grid differentiation-based core backbone network architecture construction method. The importance of lines and nodes in a power system are taken into comprehensive account, the knowledge of a risk theory is referenced, the importance of the lines is obtained by calculating risk indicators of the lines, and the importance of the nodes is evaluated based on the knowledge of a graph theory; and a core backbone network architecture searching model is constructed based on the importance of the lines and the nodes, and network architectures are searched by a particle swarm algorithm to obtain the lines and the nodes for constructing a core backbone network architecture. The importance of the lines is evaluated by line returning risks, so that the importance of the lines can be better reflected from the point of electrical quantity variation; and a fuzzy-membership-based line returning risk evaluation method more consistent with actual conditions is provided, and the influence of uncertain factors of the power system is taken into comprehensive account.

A character recognition device capable of performing accurate character recognition even when positions of characters change or adjacent characters contact or overlap each other. The device includes a character recognition image data creating unit for extracting character string image data from image data generated by using an imaging modality and repeatedly shifting the character string image in a character string direction one pixel by one pixel to create plural kinds of character string image data; and a character recognition processing unit for separating character images from each character string image to obtain character pattern data representing the separated character images, performing character recognition by using the obtained character pattern data, and calculating certainty factors based on an agreement rate of recognition results to determine a recognition result character string based on the certainty factors with respect to the plural kinds of character string image data.

The system includes system interaction management server (MS), certainty MS, data syncretizing MS, rule MS, display terminal. Connecting information integration system, the system interaction MS sands data to be interfused and relevant information to data syncretizing MS. data syncretizing MS syncretizes the inputted data through inference, and provides result for users through display terminal. In batch mode, user determines or further processes syncretized result. System returns final syncretized result back to external information integration system. Based on final syncretized result of system and QoS of each data sources in this syncretizing procedure, certainty MS modifies certainty of data sources. User can manage rule base in rule manager and certainty of each data source in data source certainty manager. Features are: legible modules, dynamic performance, generality and flexibility.

A communication apparatus includes a sensor input portion, a distributed sensor storage portion which stores sensor information from the sensor input portion in association with sensor type information or an attribute, a distributed ambient behavior processing portion which performs processing of recognition based on the sensor information stored in the distributed sensor storage portion, a certainty factor grant portion which grants a certainty factor in accordance with a result of recognition of the distributed ambient behavior processing portion, and a distributed ambient behavior storage portion which stores the result of recognition of the distributed ambient behavior processing portion as recognition information and the certainty factor granted by the certainty factor grant portion in association with the sensor information of the distributed sensor storage portion.

The invention discloses a large structural component self-adaptation machining method integrating machining, monitoring, detecting and clamping. The large structural component self-adaptation machining method is characterized in that a self-adaptation clamping device is used, self-adaptation adjusting clamping can be achieved in the machining process according to deformation of a workpiece, residual stress is released in the machining process, a displacement sensor and the clamping device are integrated, the deformation of the workpiece is monitored in real time through the displacement sensor, and feature machining sequence and tool path strategy adjustment are carried out according to the actual deformation condition; if the deformation is within the allowed range, the machining sequence is adjusted, and deformation of machining at the next step is reduced; if the deformation reaches an alarm value, on-machine testing is triggered, the tool path strategy is adjusted according to detected data, and the further-machined workpiece quality is ensured. According to the large structural component self-adaptation machining method, the deformation prediction problem based on residual stress and other uncertain factors is converted into the problem based on online monitoring, on-machine testing and other certain factors to be solved, and the final machined quality of the workpiece can be ensured fundamentally.

The invention discloses a multi-agent consistency method based on distributed adaptive event triggering. The method provided by the invention is an improved method of an existing method, and the method comprises the steps: 1) simplifying an updating step of a multi-agent control system controller protocol; 2) only updating each intelligent agent at the triggering moment of the intelligent agent, so the updating frequency of event triggering control is effectively reduced; 3) considering the influence of uncertain factors in the multi-agent control system, and proposing an event trigger-based adaptive control strategy to overcome. According to the method, complex exponential operation, acquisition of global coordinate information and complex data fusion operation are not needed, and the triggering of the event is relatively independent and only depends on the triggering moment of the event and the relative state of the neighbor node. Meanwhile, the introduction of the adaptive parameterestimation method realizes the self-estimation and self-optimization of the parameters of the multi-agent control system, and overcomes the defect that the parameter estimation depends on the uncertainty of global information during the design of the multi-agent control system.

The invention, which belongs to the technical field of automatic measurement, provides a hybrid intelligence soft measuring method of overflow granularity index in a wet grinding process. A hybrid intelligence soft measuring device for overflow granularity index in the wet grinding process includes a ball grinder, a hydraulic cyclone, a pump well, an underflow pump, a valve, a flow meter, a densimeter, a pressure meter, a belt of the grinder for feeding, a water pipe line for feeding, a feed pipe of the hydraulic cyclone, an overflow pipe, a data acquisition unit and a computer. The hybrid intelligence soft measuring method includes the steps of: (1) choosing of auxiliary variables; (2) obtaining of sampled data; (3) soft measuring of grinding granularity index based on case inference; (4) soft measuring of the grinding granularity index based on neural net; (5) solving of certainty factor based on case inference; (6) final solving of the grinding granularity index based on expert rule inference. By the method in the invention, the grinding granularity index can be estimated according to real-time data in a normal grinding process. The granularity index measuring method is the granularity measuring means having the advantages of small relative error, high credibility, high practical value and low cost.

The invention provides a contribution degree-based synchronous digital hierarchy (SDH) fault positioning method. The method is characterized by comprising steps: an alarm propagation model is constructed and an SDH alarm derivative tree is built; the contribution degree factor of each SDH alarm is calculated; fault reasons for each kind of SDH alarm data and times of the kind of SDH alarm caused by the reason are counted; the certainty factor of each kind of fault is calculated; the SDH contribution degree factors and the SDH fault certainty factors obtained after calculation are integrated to construct an SDH alarm fault reason list, and an SDH alarm rule knowledge base is constructed by the constructed SDH alarm fault reason list; and association model analysis is carried out, and SDH network fault positioning is carried out on the current SDH alarm. The method of the invention is based on the SDH fault positioning technology based on the contribution degree, faults of the topological complex SDH network can be positioned automatically, and the efficiency is higher on the basis of maintaining accuracy.

The invention discloses a machine tool cutter residual life prediction method based on LSTM + CNN, and the method comprises the steps: carrying out the judgment of the signal features of uploaded training data, and distinguishing a continuous signal and a discrete signal; performing data merging on the real-time data of different frequencies sampled by the sensor; checking whether missing values or abnormal values exist in the training data and the real-time data or not; if the missing values or the abnormal values exist, using a moving average method to supplement the missing values or replacing the abnormal values, so as to enable the data to be complete and effective, and removing outliers; carrying out selection and dimension reduction on the training data and the real-time data according to data characteristics so as to facilitate model fitting and prevent an over-fitting phenomenon; and training and testing the LSTM + CNN model, and adjusting training parameters and model parameters according to the error, so as to reduce the error to a reasonable range. According to the method, the precision of the prediction result is improved by adopting a grouping mode and a dimension reduction mode, deterministic factors and uncertain factors are comprehensively considered, and the precision of the prediction result can be effectively improved.

An object of this invention is to improve the accuracy of estimating a destination in a destination estimating apparatus. A destination estimating apparatus 100 includes: a learning data storing unit 9b that stores a history of a location specified as a destination in the past; a destination estimating unit 83 that estimates a destination from among a plurality of destination candidates including a location stored in the learning data storing unit 9b; and a candidate excluding unit 84 that, based on the history stored in the learning data storing unit 9b, excludes a destination candidate for which it is determined that a certainty factor of being a destination is lower than a predetermined threshold value from destination candidates that are estimated as being a destination by the destination estimating unit 83.

The invention discloses an SMC (Sequential Monte Carlo) algorithm based location method. The method comprises the following steps: distributing not less than four sensors for receiving acoustic emission signals emitted by an acoustic emission source on acoustic emission sources of an isotropic planar structure; converting acoustic emission source location into bayesian filtering, building a system equation and a measurement equation respectively, using Poda moment between each sub-sensor and a main sensor as a known measurement quantity and taking a source position and the wave velocity as unknown state variables. The SMC simulation algorithm is adopted to perform iterative estimation on unknown status variables due to lack of analytical solution, and an acoustic emission source is located under the influence of uncertain factors.