328 results about "Decision table" patented technology

A data structure visualization tool visualizes a data structure such as a decision table classifier. A data file based on a data set of relational data is stored as a relational table, where each row represents an aggregate of all the records for each combination of values of the attributes used. Once loaded into memory, an inducer is used to construct a hierarchy of levels, called a decision table classifier, where each successive level in the hierarchy has two fewer attributes. Besides a column for each attribute, there is a column for the record count (or more generally, sum of record weights), and a column containing a vector of probabilities (each probability gives the proportion of records in each class). Finally, at the top-most level, a single row represents all the data. The decision table classifier is then passed to the visualization tool for display and the decision table classifier is visualized. By building a representative scene graph adaptively, the visualization application never loads the whole data set into memory. Interactive techniques, such as drill-down and drill-through are used view further levels of detail or to retrieve some subset of the original data. The decision table visualizer helps a user understand the importance of specific attribute values for classification.

A packet forwarding device equipped with a statistics collection device and statistics collection method for high speed ports at a low cost while maintaining the required statistical accuracy for each flow. In order to collect a statistic flow with the required accurate statistic information this device contains a high speed flow table, a flow table made up of many flow entries, a search decision table to decide whether to search the high speed flow table or large volume flow table; and the table to be searched is decided when a packet is received. The accuracy of the statistic information for the flow requiring accurate statistic information is guaranteed by the high speed flow table. Further by setting the large volume flow table and sampling rate as needed, statistic information for many flow entries can be collected with a sufficient flow of sampled statistic information, without lowering packet forwarding performance.

Trading processes and market processes interact with each other on a platform. Each of the trading processes has a trading strategy embodied in a decision table. Each of the market processes has a market methodology that may employ a decision table in like manner as the trading processes. Accordingly, the trading processes and market processes are easy to configure. The decision table comprises rules having conditions and actions that are to be performed when the conditions are true.

Systems and methods for developing business rules for decision engines are provided. A user interface may be provided for inputting business rules and one or more sets of data. The business rules may be in a format of a decision table and/or a decision matrix. The business rules may be applied on the sets of data to generate rule feedback, using an internal decision engine, which denotes coverage of the data by the business rules. The rule feedback may be provided on the user interface, such as with a data coverage indicator utilizing colors, and/or data coverage statistics. The business rules may be defined and validated prior to implementation in an external decision engine. Importable business rules that are adapted to be executed by the external decision engine may be generated based on the business rules. Components of the system may be executed locally on a personal computer.

The number of content addressable memory (CAM) lookups is reduced from two to one. Each side (left and right sides) of a CAM is programmed with network addresses, such as IP addresses, based on certain bits of the network addresses. These bits of the network addresses (which represent packet routes) are examined and used to determine whether the particular network address is to be placed on the left or right sides of the CAM. The grouping of certain network addresses either on the left or right sides of the CAM can be performed by examining an individual bit of each network address, by performing an exclusive OR (XOR) operation on a plurality of bits of each network address, and/or by searching for bit patterns of the network address in a decision table. Network addresses that cannot be readily assigned to a particular side of the CAM using these grouping techniques are programmed into both sides of the CAM. During packet routing, techniques similar to the grouping techniques that populated the CAM are used to determine which of the two sides of the CAM is to be searched.

The present invention is directed to a method and mechanism for using rules metaphors to implement rules and ruleflows. Examples of such rules metaphors include decision tables, trees, scoreboards, state transition diagrams, and questionaires. By providing a choice of different rules metaphors, a rule developer can advantageously tailor the framework by which rules are created and accessed to more closely match the specific processes or task being addressed.

The invention relates to an EHV (Extra-High Voltage) power grid fault rule mining method based on a rough set association rule. According to the method, a distributed data mining idea is adopted, historical fault data of an EHV power grid is subjected to fault rule mining by using an association rule mining method based on a rough set theory, a distributed decision table is subjected to attribute reduction by using an attribute reduction algorithm based on an information entropy, and then, an Apriori algorithm in association rules is applied to the decision table, which is subjected to reduction, so as to carry out fault rule extraction. According to the method, the problem of inadaptability to large-data-volume historical fault databases of traditional data mining methods can be solved effectively, the complexity of rule extraction is lowered, and the method has the advantage of high fault rule mining efficiency.

The invention relates to a real-time online tool wear monitoring method which comprises the steps of: firstly, under different cutting parameters, respectively acquiring acoustic emission signals in different wear states, current signals of a spindle motor and a feed motor in a machine tool, cutting speed, cutting depth and feed rate; taking the acquired values as condition attributes, and establishing a decision table; carrying out discretization treatment on the continuous attribute values in the decision table by adopting a self-organizing map neural network; reducing the number of the attributes by adopting the Johnson algorithm; optimizing the initial weight value and the threshold values of a back propagation (BP) neural network by the genetic algorithm, and using the reduced attribute value which is taken as an input neuron of the BP neural network to train and learn the optimized BP neural network; using the trained BP neural network to predict the tool wear degree. The method not only simplifies the structure of the neural network so as to enable the neural network to have rapid neural astringency and stronger learning ability, but also improves the recognition rate of tool wear detection.

The invention relates to a knowledge acquisition method of the fault diagnosis knowledge base of a turn-milling combined machine tool. The method comprises the following steps: 1. obtaining the fault history information and real-time monitoring information of the turn-milling combined machine tool; 2. performing signal processing, fault characteristic information extraction and data discretization to the fault history information and real-time monitoring information in turn, establishing a knowledge decision table of fault diagnosis by using fault characteristic attribute as condition attribute and fault pattern as decision attribute; 3. based on the established knowledge decision table, performing the reduction based on granule computing of fault characteristic attribute and the reduction of attribute value; and 4. using the confidence and coverage of the rules as evaluation indexes to measure and evaluate the reduced decision. The invention can obtain reduced and effective knowledge and rules for fault diagnosis from a great deal of on-line and off-line data so as to provide effective guarantee for fault diagnosis. The invention can be widely used for the fault diagnosis of various numerical control machine tools.

There is provided an information processing device including: a storage unit that has an attribute table storing attribute values given to a plurality of contents; and an importance calculation unit that calculates an importance to a specific attribute of content with respect to one or more attributes other than the specific attribute of content using attribute values stored in the attribute table, wherein the importance calculation unit calculates the importance using a decision table with the specific attribute as a decision attribute and the one or more attributes as condition attributes.

A computer-readable recording medium on which a document management program capable of calculating an evaluation value for objectively judging information transparency in an organization is recorded. When an original document is inputted, a document input section stores the document in a document store section. After that, a document correction section corrects a character string of the document stored in the document store section in response to operation input and registers a correction history including a number of characters deleted by a character string deletion process and a correction history including a number of characters before replacement by a replacement process in a correction history table. A correction history analysis section uses an opacity rate the value of which becomes higher with an increase in the number of the characters deleted from the document or an increase in the number of the characters before the replacement by the replacement process and makes a decision table including the opacity rate.

The invention relates to a mixed scheduling method of an embedded real-time operating system. The method comprises the steps of pre-task scheduling resource allocation and task scheduling, wherein the task scheduling comprises the following steps: enabling a task to be ready in an event-trigger or time-trigger mode; looking for a priority decision table based on a ready list and a ready group to obtain the current ready highest task priority; and when the ready task of the highest priority is a collaborative task, directly running the collaborative task, removing the positions of the collaborative task in the ready list and the ready group after the completion of the collaborative task, and executing the previous step again. By treating ISR as the task, allocating priority along with other tasks, simultaneously combining the time-trigger and event-trigger framework and adopting the mixed scheduling mode of preemptive and collaborative tasks, the invention obtains a scheduling method which has the advantages of high predictability and reliability and high real-time response capability. The scheduling method can make full use of the system resources and effectively control the system overhead.

The invention relates to a rail transit diagnosis method and system based on a rough set. The method comprises the following steps: (1) collecting monitoring data of rail transit signal equipment and extracting the characteristics of the collected monitoring data so as to establish a fault diagnosis decision table, (2) based on the rough set, conducting knowledge extraction and attribute reduction on the fault diagnosis decision table so as to obtain a best attribute reduction combination, (3) establishing a neural network model, using the condition attribute in the best attribute reduction combination as input, using the decision attribute of the best attribute reduction combination as the output target of a neural network, and adopting the neural network for training, (4) using the trained neural network for calculating the possibility of a possible fault area of the real-time fault information, using the fault area largest in possibility as a fault diagnosis result and outputting the result. The rail transit diagnosis method can solve the problems that work load is large, efficiency is low, and risk performance is high when rail signal system failures are judged manually, and improves the efficiency and the accuracy of rail transit data analysis and failure diagnosis.

The invention discloses a data mining (DM) method of a rough set and optimization neural network, which is characterized by comprising the following steps: a. analyzing sample data and forming an initial continuous attribute decision table according to the known domain knowledge; b. dispersing the continuous attribute through a dispersion method to form a dispersion attribute decision table; c. reducing the dispersion attribute decision table; and d. training the data by a neural network. The method of the invention can reduce the data amount required by the network learning, and further enhance the DM efficiency of the large actual data base by the application of the rough set and neural network.

The invention discloses a short-term load predicting method based on a quick fuzzy rough set. The method comprises the following steps that firstly, electrical load data recorded by an electricity meter installed in a power grid are collected, and an initial attribute decision table is constructed; secondly, a fuzzy subordinate function of the condition attribute and the decision attribute is determined; thirdly, the attribute reduction is carried out according to the quick fuzzy rough set method, and the reduction condition attribute is obtained; fourthly, the reduction condition attribute serves as input data of a neural network to train normalized historical load data; fifthly, the neural network obtained through training is utilized for carrying out the short-term load prediction on an electric power system; sixthly, reverse normalization processing is carried out on the obtained normalization value of the maximum load of the prediction day, and a short-term electric power load prediction result is obtained and is the maximum load of the prediction day. The computing amount of the fuzzy rough set attribute reduction is small, the computing time is short, and the computing efficiency is improved.

The invention relates to a knowledge base construction method oriented to fault diagnosis and fault prediction of a numerical control machine tool. The method comprises the following steps of: step 1, performing real-time monitoring on a high-grade turning center through a remote monitoring device, and obtaining multiple groups of vibration data Xj(t) representing different fault types, wherein j is the number of acquired vibration data groups, and n is a positive integer; step 2, orderly executing temporal rough wavelet packet analysis on the multiple groups of vibration data Xj(t), obtaining an energy feature vector T' as a condition attribute, and taking the fault type as a decision attribute to construct a fault knowledge primary decision table; step 3, executing discernibility matrix-based fault feature attribute reduction on the fault knowledge primary decision table to generate a rule and form a knowledge base; and step 4, taking the confidence level of the rule as an evaluation index to measure and evaluate the final rule. The method provided by the invention can provide effective guarantee for fault diagnosis and fault prediction, and can be widely used in the high-grade turning center.

The invention discloses an attribute reduction method and a mental state assessment method on the basis of a genetic algorithm and a rough set. According to the rough set attribute reduction method on the basis of the genetic algorithm and the rough set, a proper fitness function is set, the application range of the attribute reduction method based on the genetic algorithm and the rough set is widened, and key indicators with concentrated attributes in a decision table can be rapidly and effectively obtained. According to the mental state assessment method, during mental state assessment, the key indicators with the concentrated attributes in the decision table are extracted by the attribute reduction method based on the genetic algorithm and the rough set, and a bayesian network is built and trained according to an extraction result to obtain a classification model for performing mental state assessment. By means of the attribute reduction method and the mental state assessment method on the basis of the genetic algorithm and the rough set, the efficiency of mental state assessment is greatly increased, the accuracy is good, the implementation is easy, and wide adaptability to data can be achieved.

A risk assessment system and a data processing method are provided. The risk assessment system includes an analyze device and an electronic device. The analyze device generates at least one decision table according to a plurality of data and context features of the plurality of data. Each of the decision table has a plurality of entries, and each of the entries includes at least one of determining condition and probability information corresponding to a specific result. The electronic device communicates with the analyze device. The electronic device receives the decision table and compares the determining conditions of each entries with current conditions of an assessee. When the current conditions are the same to the determining conditions of a specific entry, the electronic device displays the determining conditions and the probability information corresponding to the specific entry.

Trading processes are operative to route orders from order rooms to market processes, which process the orders according to respective market methodologies. The order routing strategy can be embodied in a decision table having rules with conditions and actions to be taken when the conditions are true. Accordingly, order rooms can readily configure and reconfigure trading processes.

The invention requests to protect a neighborhood rough set ensemble learning method based on attribute clustering, and relates to a data mining technology. First, the condition attributes of a decision system are divided into multiple clusters through attribute clustering, wherein the correlation between the attributes in the attribute clusters is large, and the correlation between different attribute clusters is small; second, different base classifiers are trained and integrated based on the difference between the clusters, guidance of a neighborhood rough set is added to the process of base classifier training and integrating, and the weights of the base classifiers are allocated according to the identification ability of the base classifiers to samples in the boundary region of the neighborhood rough set so as to get a final integrated classifier; and finally, test sets are classified by the obtained integrated classifier. According to the invention, a neighborhood rough set and the theory of ensemble learning are combined, the correlation and the difference between the condition attributes in a decision table are fully utilized, different base classifiers complement each other, and the knowledge in the decision system can be mined effectively from different angles.

The invention belongs to the technical field of embedded operating systems, in particular relating to a thread dispatching implementation method based on an on-chip multiprocessor. In the invention, athree-dimensional ready bitmap is used as a basis, the ready bitmap is shared by all processors, and each ready thread is marked on the bitmap, thus a thread allocation algorithm based on the bitmapfor an operating system is provided, and the operating system operates on one processor core. The invention greatly improves the real-time performance of the system by using the three-dimensional bitmap, a registry, a decision table, and the like, and simultaneously allocates multiple threads on a plurality of processor cores to be processed in parallel, thereby improving the whole execution speed.

The invention discloses a method of identifying deviation of a welding joint of rotating electric arc gas shielded welding based on a rough set. The method utilizes a welding electric arc electric signal sensor, a welding electric arc position sensor, a data collecting card and the like to collect experimental data. Under a rough set modeling mode, the collected experimental data are used for constructing a decision table through a data preprocessing module, and a rough set model is constructed through an attribute reduction module, an attribute value reduction module and a rule reduction module. Under an online deviation prediction mode, the collected experiment data undergo processing by the data preprocessing module and are matched with the rough set model constructed under the rough set modeling mode. In addition, uncertain reasoning is utilized to predict deviation of the welding joint, and the deviation of the welding joint is output through a statistical module. According to the method of identifying deviation of the welding joint of rotating electric arc gas shielded welding based on the rough set, nonparametric modeling of identification of the deviation of the welding joint is achieved, system composition is simple, anti-interference capability is strong, engineering practicality is good, the welding joint deviation can be extracted in real time, and the method can be applied to a corresponding deviation device for real-time deviation.