135 results about "Reasoning system" patented technology

The invention provides a method, apparatus and algorithm for data processing that allows for hypothesis generation and the quantitative evaluation of its validity. The core procedure of the method is the construction of a hypothesis-parameter, acting as an "ego" of the non-biological reasoning system. A hypothesis-parameter may be generated either based on totality of general knowledge facts as a global description of data, or by a specially designed "encapsulation" technique providing for generation of hypothesis-parameters in unsupervised automated mode, after which a hypothesis-parameter is examined for the concordance with a totality of parameters describing objects under analysis. The hypothesis examination (verification) is done by establishing a number of copies of a hypothesis-parameter that may adequately compensate for the rest of existing parameters so that the clustering could rely on a suggested hypothesis-parameter. The method of this invention is based on the principle of the information thyristor and represents its practical implementation. This invention can be used as a universal computer-based recognition system in robotic vision, intelligent decision-making and machine-learning.

A case-based reasoning system includes a case database capable of storing a plurality of cases that each include one or more attributes. A view generator in the case-based reasoning system generates a view of the case database by representing each case within at least a selected subset of the plurality of cases within the case database with one or more uniform-length view tokens. An input parser in the case-based reasoning system provides a tokenized representation of an input incident that includes one or more input tokens. The case-based reasoning system further includes a search engine that compares the input tokens with the view tokens to identify one or more closely matching cases within the view. By searching the view rather than directly searching the case database, cases that closely match the input are efficiently identified.

The invention discloses a self-adaptive tracking loop, which comprises an unscented Kalman filter (UKF), an observation noise variance matrix detection module, a fuzzy inference system, an unscented transformation (UT) scale factor regulation module, a state compensator, a carrier wave numerical controlled oscillator (NCO), scale factors, a code NCO, an integration and zero-clearing module, a code loop phase discriminator and a second order code loop filter, and additionally discloses an implementation method for the self-adaptive tracking loop. The implementation method comprises a step 1 ofsignal correlation, integration and zero clearing; a step 2 of code phase tracking; a step 3 of UKF modeling; a step 4 of observation noise variance matrix estimation; a step 5 of process noise variance matrix estimation; a step 6 of UT scale factor regulation; a step 7 of state estimation deviation compensation; and a step 8 of assistance of the carrier wave NCO in the code NCO. According to theself-adaptive tracking loop, the UKF, the observation noise variance matrix detection module and the fuzzy inference system are designed in the carrier tracking loop, so not only can a contradiction between thermal noise vibration in the tracking loop and a dynamic stress error be solved, but a process noise variance matrix and an observation noise variance matrix can be regulated in a self-adaptive manner according to changes of the external environment, and thereby the self-adaptive ability of the tracking loop under complex changeable environments of high dynamic, strong interference, and the like is effectively improved.

A case-based reasoning system is disclosed that includes a case database and a search engine. The case database is capable of storing a plurality of cases that each include one or more attributes that each have an associated match weight. Match weights of attributes in different cases are separately specified. In response to receipt of an incident including one or more input terms, the search engine scores the relative closeness of a selected case to the incident utilizing the match weights of attributes in the selected case that match input terms in the incident.

The invention discloses a fusion reasoning system and a fusion reasoning method for intelligent tags of news programs, relates to the technical field of news program tags. the intelligent news programtag identification system comprises an intelligent identification actuator, a historical tag library, an internal knowledge base, an internal case library and an analysis reasoner; the intelligent identification actuator executes identification tasks of various news program materials, and basic tag extraction is conducted on video images, voice and text information; Wherein the historical tag library stores materials, metadata and tags; The internal knowledge base is used for supplementing an intelligent recognition result and providing more information for subsequent analysis and reasoning;Wherein the internal case library is a case set established based on a historical tag library; According to the news program tag automatic fusion reasoning system and method, the intelligent recognition method is comprehensively utilized, the internal knowledge base and the internal case base are established based on the historical tag base, automatic fusion reasoning of news program tags is completed, and classification is accurate and efficient.

A mechanism is provided for achieving autonomic computing self-healing utilizing meta level reflection. In a base level, a monitor detects an error in a production environment. The base level provides a reification message comprising data about the error to a meta level. A reasoning system in the meta level receives the reification message and analyzes the data using knowledge of computational components in the base level. The reasoning system identifies a self-healing action for the error and returns a reversion message comprising a signal to implement the self-healing action. Responsive to receiving the signal, the base level implements the self-healing action.

A fuzzy inference system for use in modulating radiation treatment includes a fuzzifer for inputting imaging data, and inference device operatively to the fuzzifer for analyzing the imaging data and determining radiation treatment target from non-treatment target, and a defuzzifier for modulating radiation treatment pursuant to the analysis from the inference device.

The invention discloses an error compensating method of a photoelectric encoder. An error compensating process of the method is simpler, so as to realize the aim of improving the compensation precision. Equipment provided by the method comprises the photoelectric encoder, a rotating table and a controller. The error compensating method comprises the following special steps of: obtaining y by subtracting z by x, wherein each angle measurement value of the controller is regarded as a value x to be compensated, and each corner value of the rotating table is regarded as a conventional true value z; according to the x and the z, establishing a knowledge base model of the photoelectric encoder by using a least square method, and outputting z'; according to the x and the y, establishing a neural fuzzy model by using a structure of an improved self-adaption neural fuzzy reasoning system, and outputting ynet(k); adding outputs of the established knowledge base model and neural fuzzy model, so as to obtain z'+ynet(k); and respectively inputting each measurement value of the photoelectric encoder to the established knowledge base model and neural fuzzy model so as to compensate, and outputting the compensated values by the controller.

A dependency graph of rule predicates without strongly connected sub-graph is obtained. The dependency graph indicates the dependency among the rule predicates. An update frequency of node in the dependency graph is calculated, and a query frequency of node in the dependency graph is also calculated. Furthermore, a runtime query cost value and a materialization cost value of the node are calculated based on the query frequency and update frequency. Node to be materialized are determined based on the runtime query cost value and the materialization cost value. A rule predicate corresponding to the node to be materialized is the rule predicate to be materialized. In at least some instances, an exemplary technical effect is that the return time of result of runtime query is saved and the affect by the data update is reduced when a query is performed in relation data reasoning system constructed with rule predicates.

Te invention discloses a battery SOC estimation method applying multiple models parallelly. The method includes following steps: 1), acquiring battery state parameters; 2), normalizing the battery state parameters; 3), respectively substituting the battery state parameters after being normalized into a genetic algorithm optimized BP neural network estimation model, a self-adaptive neural-fuzzy reasoning system model and an OS-ELM neural network model to acquire three SOC estimation results; 4), according to a SOC-OCV relation, acquiring initial estimation voltage values respectively corresponding to the three SOC estimation results acquired in the step 3); 5), respectively calculating difference values between the initial estimation voltage values and voltage, and normalizing the difference values to acquire weighting coefficient of the initial estimation voltage values; 6), calculating a final estimated value of SOC according to the weighting coefficient. By utilizing three importantparameters of a battery, using the three models parallelly, using multiple linear system estimation results to describe a nonlinear system and then weighting, summing and estimating the battery SOC value, estimation accuracy is improved effectively.

Systems, methods, and computer program products that enable system-wide probabilistic forecasting, alerting, optimizing and activating resources in the delivery of care to address both immediate (near real-time) conditions as well as probabilistic forecasted operational states of the system over an interval that is selectable from the current time to minutes, hours and coming days or weeks ahead are provided. There are multiple probabilistic future states that are implemented in these different time intervals and these may be implemented concurrently for an instant in time control, near term, and long term. Those forecasts along with their optimized control of hospital capacity may be independently calculated and optimized, such as for a dynamic workflow direction over the next hour and also a patient's stay over a period of days. In the present application, a probabilistic and conditional workflow reasoning system enabling complex team-based decisions that improve capacity, satisfaction, and safety is provided. A means to consume user(s) judgment, implement control on specific resource assignments and tasks in a clinical workflow is enabled, as is the dynamical and optimal control of the other care delivery assets being managed by the system so as to more probably achieve operating criteria such as throughput, waiting and schedule risk.

The invention discloses a self-adaption strong tracking unscented kalman filter (UKF) positioning filter algorithm based on fuzzy logic. The self-adaption strong tracking UKF positioning filter algorithm comprises the steps that: (1) a positioning filter model is built; (2) initial parameters of a filter are set; (3) the state quantity is subjected to filtering by adopting the UKF filter algorithm; (4) a fuzzy logic system is used for solving softening factors in the self-adaption tracking algorithm; (5) the self-adaption factors in the strong tracking self-adaption algorithm are solved; and (6) the epoch moment is increased by 1, the next moment observation is read, and the operation returns to the step (4) until the operation is completed. The strong tracking self-adaption algorithm in introduced on the basis of the UKF filter algorithm, in addition, a novel recursive algorithm is adopted in the strong tracking self-adaption algorithm for estimating the information covariance matrix, and the softening factors in the strong tracking algorithm are solved through a fuzzy logic reasoning system and are estimated in real time according to the work state of an epoch moment filter. The estimation is carried out in satellite navigation user receiver position estimation, and the positioning performance and the capability of carriers adapting to the dynamics can be greatly improved.

The invention discloses a knowledge reasoning system and method based on the social network knowledge mapping. The system comprises a data source module, a multigraph network engine module, a knowledge mapping discovery module, a distributed graph database module, and a core sub-algorithm module; the data source module, the knowledge mapping discovery module, the distributed graph database module, and the core sub-algorithm module are all connected with the multigraph network engine module, and the distributed graph database module is connected with the core sub-algorithm module. According to the knowledge reasoning system and method based on the social network knowledge mapping, more social relationships can be discovered through the knowledge mapping of the social networks, hidden relationships in the knowledge maps of the social networks can be found as needed, according to the algorithm model, the graph models are periodically and incrementally updated, and more innovative and more accurate relationship models are obtained.

The invention discloses a cloud configuration safety assessment method. The cloud configuration safety assessment method comprises the following steps: (1), performing qualitative and quantitative analyzing on security problems and security evidences in the hierarchical structure of each service configuration including an IaaS layer, a Paas layer and an Saas layer; (2), assessing the security of each service configuration of a cloud platform; and (3), assessing the overall security of the cloud platform, establishing a first-level cloud security assessment model by use of a Sugeno fuzzy inference system based on an adaptive neural network, and analyzing the overall security of the cloud platform. Compared to the prior art, the number of users accessed to a network end is dynamically and reasonably controlled, loads accessed to the network end are always maintained within a certain acceptable scope, the users can be dispersed when a large number of users are simultaneously accessed to a network at the same frequency, the users can be accessed to the network as soon as possible, and the system random access performance in case of network congestion is greatly improved.

A system that uses inferencing to deal with complex authentication problems such as those that occur when users authenticate themselves by means of tokens. A troubleshooter responds to a user's request for help by inputting an identification code for the user's token into the system. The system uses the identification code to obtain the current authentication state for the token from an authentication database and then inputs the authentication state to an inferencing system such as a knowledge base. The inferencing system uses the authentication state to make inferences concerning the problem. The inferencing process may involve obtaining further information from the troubleshooter an / or the authentication database and may also provide warnings of particular security problems to the troubleshooter. Eventually, the inferencing system reaches a solution, which may include advice that the troubleshooter is to give to the user. The solution may also involve a modification to the authentication database. When that is the case, an indication from the troubleshooter that the system is to modify the database causes the system to automatically perform the modification. In a preferred embodiment, communication between the troubleshooter and the system is via World Wide Web protocols.

A system, method, and article for profiling an applicant, an examiner, and agency or examination group for the purposes of examination of an application that undergoes a regulated or structured examination. One aspect of the invention can provide a greater degree of predicting success or potential success based on identifying a data factor that has a potential influence upon success of the application. Another aspect uses statistical methods or logic from a fuzzy inference system to analyze data factors. One example is directed towards the filing and prosecution of intellectual property documents such as patent and trademark applications.

The invention discloses a voltage sag source identification method based on Mamdani fuzzy reasoning, belonging to technical field of voltage quality monitoring of a power system. The method comprises the steps of calculating the membership degree of three-phase voltage to balance, the membership degree of the voltage at the end of voltage sag to mutation degree and the membership degree to harmonic content; inputting the membership degrees to an Mamdani fuzzy reasoning system and substituting into fuzzy rules to calculate a corresponding fuzzy result; and using the center of gravity defuzzification for defuzzifying the fuzzy result to obtain the type of a sag source. The method takes into account of inherent differences of four different sag causes and is started from amplitude, mutation and harmonic, thereby avoiding the impacts of a plurality of uncertain features on the analysis accuracy of different sag sources; in addition, the method fuzzifies the input and the output quantities of the reasoning system, thereby avoiding the practice that the prior art needs to determine thresholds of all the feature values and further reducing the identification errors caused by the fixation of the thresholds.

A system for deriving ontologies to support inferencing with changing context, including changes of time. Embodiment of the invention use a unique system for modeling context and the interactions among multiple contexts in order to compute functions that can modify ontologies for presentation to a reasoning system. A parallel unique system allows previous inferences to be retrospectively modified based on newly derived ontological semantics. The system allows for the creation of new ontological elements and auditable models of agency and cause. It can be implemented using methods that delay evaluation until semantic interpretation is required, either at the ontological or inferential level.

The present invention relates to a valuation-based learning system. The system is configured to receive a plurality of inputs, each input being input evidence corresponding to a variable in a Dempster-Shafer Reasoning System. The Dempster-Shafer Reasoning System is a network of interconnected nodes, with each node representing a variable that is representative of a characteristic of a problem domain. A discount weight is then optimized for assigning to each of the inputs. A basic probability assignment (bpa) is generated using the Dempster-Shafer Reasoning System, and where the bpa is an output for use in determining a solution of the problem domain. Finally, a solution to the problem domain is determined using the bpa.

The invention relates to an online model reasoning system. According to the embodiment of the invention, the method comprises the steps of: establishing a model warehouse and a container mirror imagewarehouse in which container mirror images required by an inference model are stored; when receiving an online reasoning request of a user, enabling a model micro-service engine to call an inference model required by the user from a model warehouse according to the configuration information of the user; and storing the container mirror image in the container mirror image warehouse, so that the situation that the container mirror image required by the training model is inconsistent with the actual container mirror image is avoided, the inference model can be packaged into inference service capable of being operated in a containerization manner, and online inference service is provided.

The invention relates to a system for intelligent decision-making of concrete dam pouring construction based on knowledge engineering. The system comprises an engineering information base, a construction rule base, an intelligent reasoning system and a function module system. The system for intelligent decision-making of concrete dam pouring construction based on knowledge engineering, and the method for intelligent decision-making of concrete dam pouring construction based on knowledge engineering, both created by the invention, can realize management and decision-making of concrete dam pouring construction through the application of the methods such as knowledge representation, knowledge acquisition and knowledge reasoning of the knowledge engineering and the intelligent reasoning system, with the premise that the technology specification and requirement of concrete dam pouring construction are complied.

The invention discloses an early warning method for a bridge structure strain response exception. The method comprises the following steps that: utilizing a wavelet packet decomposition method to separate a bridge structure strain response; (2) utilizing a principal component analysis method to extract the principal component of a bridge ambient temperature field; (3) on the basis of an adaptive neural network fuzzy inference system, establishing a complex nonlinear relationship between an actual measurement load factor and corresponding strain data; (4) identifying the position information ofa vehicle on a bridge; (5) identifying the geometric parameter and the axle load of the vehicle; (6) on the basis of the adaptive neural network fuzzy inference system, establishing a complex nonlinear relationship between an actual measurement vehicle load parameter and corresponding strain data; (7) solving a bridge structure strain response theoretical value; and (8) comparing a theoretical solving result of the bridge structure strain response and the actual measurement result of the theoretical solving result, and updating the adaptive neural network fuzzy inference system. By use of themethod, effectively according to the actual measurement load parameter, the bridge structure strain response can be accurately predicted.

Certain examples provide systems and methods to monitor and control hospital operational systems based on occupancy data and medical orders. An example healthcare workflow management and reasoning system includes a workflow engine including a first particularly programmed processor to monitor one or more medical orders from one or more hospital information systems to identify a condition indicating that a first patient in a first room is ready for a clinical activity such as discharge. The example healthcare workflow management and reasoning system includes a sensing component including a second processor to gather occupancy data regarding the first patient in the first room and transmit the occupancy data to the workflow engine. The example workflow engine controls one or more hospital operational systems to trigger cleaning of the first room, lighting settings for the first room, and transportation of a second patient to the first room based on occupancy data from the sensing component.

The invention discloses an airspace operation situation evaluation and classification method based on fuzzy reasoning and belongs to the technical field of airspace situation evaluation and classification. The method comprises the following steps: 1, processing airspace operation situation samples in a to-be-processed sector; 2, establishing a primary fuzzy reasoning system based on the airspace operation situation samples in the to-be-processed sector; 3, optimizing interpretability and accuracy of the fuzzy reasoning system on the basis of a multi-target population adaptive immune algorithm.The method provided by the invention can aim at large-scale and high-dimensionality sector operation data and center on airspace operation situation evaluation accuracy and interpretability, the multi-target immune optimization algorithm is used, and the airspace situation evaluation accuracy is optimized. In addition, when the immune algorithm is realized, the condition that the fuzzy matrix scale is in exponential growth in the process of processing high-dimensionality data is avoided, time complexity and space complexity needed by the algorithm are greatly reduced, and the convergence precision is improved.

The invention discloses a program static automatic analysis method and relates to a method of automatically analyzing program property by using a data flow analysis means and a logic reasoning system.The method includes: using a grammar analysis tool to analyze a source code, and constructing an extension program flow graph on a sentence level; using a recursive function and property thereof to define property of a program to be analyzed, and using a meet semi-lattice to describe a valuing set of the property; customizing a processing procedure when the program property is transmitted and passes a program sentence, performing automatic derivation and analysis by combining a constraint solving tool with logic reasoning rules, and using a data flow analysis algorithm to execute automatic iteration to solve an analysis result; writing the result back onto the program node of the extension flow graph in a formula form for the convenience of analysis personnel to judge whether the code is wrong or not.By using the method, manual analysis operation can be replaced, analysis accuracy and completeness are guaranteed, errors and omissions caused by manual analysis are avoided, and time cost and manpower cost are saved effectively.

The present invention relates to a general-purpose analogical reasoning system. More specifically, the present invention relates to a high-performance, semantic-based hybrid architecture for analogical reasoning, capable of finding correspondences between a novel situation and a known situation using relational symmetries, object similarities, or a combination of the two. The system is a high-performance symbolic connectionist model which multiplexes activation across a non-temporal dimension and uses controlled activation flow based on an analogical network structure. The system uses incremental inference to stop inference early for object correspondence, uses initial mappings to constrain future mappings, uses inferred mappings to synchronize activation, and independent mapping based on roles, superficial similarity, or composites.

The invention provides an intelligent reasoning system and method for a drowning mode. The system comprises a terminal and a server, wherein the terminal is connected with the server, and the server comprises a receiver, a classifier and a comprehensive processor. The classifier is respectively connected with the receiver and the comprehensive processor, and the receiver is connected with the comprehensive processor. The receiver is used for forwarding data information sent by the terminal to the classifier and the comprehensive processor, the classifier is used for carrying out training analysis on the data information sent by the receiver to obtain a classification model and judging whether the user is drown or not according to the classification model, and the comprehensive processor is used for returning the refined classification model to the classifier according to the data provided by the receiver and the classification model of the classifier.