223 results about "Inference system" patented technology

This invention teaches a multi-sensor quality inference system for additive manufacturing. This invention still further teaches a quality system that is capable of discerning and addressing three quality issues: i) process anomalies, or extreme unpredictable events uncorrelated to process inputs; ii) process variations, or difference between desired process parameters and actual operating conditions; and iii) material structure and properties, or the quality of the resultant material created by the Additive Manufacturing process. This invention further teaches experimental observations of the Additive Manufacturing process made only in a Lagrangian frame of reference. This invention even further teaches the use of the gathered sensor data to evaluate and control additive manufacturing operations in real time.

A search engine for databases, data streams, and other data sources allows user preferences as to the relative importance of search criteria to be used to rank the output of the search engine. A weighted preference generator generates weighted preference information including at least a plurality of weights corresponding to a plurality of search criteria. A weighted preference data search engine uses the weighted preference information to search a data source and to provide an ordered result list based upon the weighted preference information. A method for weighted preference data searching further includes allowing a user to re-rank the ordered result list and determine newly updated weighted preferences based on the re-ranking process. A plurality of weights signifying the relative importance of the search criteria may also be generated by the user inputting an ordered list. This ordered list may be a single alternative or a plurality of alternatives.

A method and system for computer assisted automatic error detection and diagnosis of file servers. Software modules periodically and continuously review monitoring statistics gathered by the file server regarding its operation. The monitoring statistics are processed by a pattern matching system and a rule-based inference system. Software modules augment known network protocols, by manipulating parameters of lower-level protocols using different higher-level protocols. Using the higher-level protocols, software modules determine if the selected parameters for the lower-level protocols are correct. Software modules track hardware and software configuration changes to the file server, and relate changes in known monitoring statistics to timing of those hardware and software configuration changes.

The parallel kinematic machine (PKM) trajectory planning method is operable via a data-driven neuro-fuzzy multistage-based system. Offline planning based on robot kinematic and dynamic models, including actuators, is performed to generate a large dataset of trajectories, covering most of the robot workspace and minimizing time and energy, while avoiding singularities and limits on joint angles, rates, accelerations and torques. The method implements an augmented Lagrangian solver on a decoupled form of the PKM dynamics in order to solve the resulting non-linear constrained optimal control problem. Using outcomes of the offline-planning, the data-driven neuro-fuzzy inference system is built to learn, capture to and optimize the desired dynamic behavior of the PKM. The optimized system is used to achieve near-optimal online planning with a reasonable time complexity. The effectiveness of the method is illustrated through a set of simulation experiments proving the technique on a 2-degrees of freedom planar PKM.

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.

Methods and apparatus for color correction of color image data obtained by an electronic camera determine a correction to data representative of the color image based upon an estimated illuminant using a neural network, multilayer perceptron models and/or coactive neuro-fuzzy inference system models, and apply the correction to the data representative of the color image. Data representative of the color corrected data may be recorded or transmitted. A method of recording image data obtained by an electronic camera captures a color image, generates data representative of the image, estimates an illuminant for the captured color image, generates data representative of the estimated illuminant and records the data representative of the image with the data representative of the estimated illuminant. A method of transmitting image data obtained by an electronic camera captures a color image, generates data representative of the image, estimates an illuminant for the captured color image, generates data representative of the estimated illuminant and transmits the data representative of the image with the data representative of the estimated illuminant.

The present invention discloses an improved system and method for specifying and compiling computer programs. Type aliases are introduced whose binding is inferred by a type inference component during compilation. Once declared, type aliases can be utilized just like regular types thereby providing added efficiency in coding, among other things. Additionally, a mechanism for specifying the introduction of a new variable whose type is to be inferred is disclosed. This mechanism clears up an ambiguity during type inference concerning whether to infer a new variable type or utilize a variable in scope. Further yet, an efficient type inference system and method is disclosed to effectively deal with overloading among other things.

A method for dynamically quantizing feature maps of a received image. The method includes convolving an image based on a predicted maximum value, a predicted minimum value, trained kernel weights and the image data. The input data is quantized based on the predicted minimum value and predicted maximum value. The output of the convolution is computed into an accumulator and re-quantized. The re-quantized value is output to an external memory. The predicted min value and the predicted max value are computed based on the previous max values and min values with a weighted average or a pre-determined formula. Initial min value and max value are computed based on known quantization methods and utilized for initializing the predicted min value and predicted max value in the quantization process.

The invention provides an autonomous underwater vehicle (AUV) vertical plane under-actuated motion control method, which comprises the steps that: (1) initialization setting is performed on the AUV; (2) a top-layer control computer transmits a mission and finishes the global planning; (3) a motion control computer receives the feedback information of a sensor, performs control calculation by using a self-adaptive neuro-fuzzy inference system-based auto-disturbance rejection controller, and outputs a control command, namely an elevator angle delta s of the stern; (4) a steering engine executes the control command, finishes the coordination control of AUV depth and pitch attitude, and realizes the motion control under the AUV vertical plane under-actuated constraint; and (5) whether the mission is finished is judged, if the mission is finished, the data is saved and the voyage is ended, and if the mission is not finished, the control command is continuously calculated by the motion control computer. The AUV vertical plane under-actuated motion control method is suitable for the under-actuated, strong-coupling and complex motion relationship of the AUV in the vertical plane motion process, and can realize precise motion control.

Intelligent technique is an effective method to perform the network resource management. A three layer cascade adaptive neural fuzzy inference system (ANFIS) based intelligent controller is proposed for the mobile wireless network to optimize the maximum average throughput, minimum transmit power and interference for multimedia call services. The proposed intelligent controller is designed with a three layer cascade architecture, which mainly contains an ANFIS rate controller (ARC) in the first layer, an ANFIS power controller (APC) in the second layer and an ANFIS interference controller (AIC) in the third layer. The design aim of the proposed three layer cascade ANFIS cognitive engine is maximizing the average throughput of the mobile wireless network, while minimizing the transmit power and interference power.

The invention discloses a method for detecting TCP/IP protocol covert channel based on fuzzy neural network. The method adopting the fuzzy neural network introduces a novel method for detecting the network covert channel by analyzing a domain in a TCP/IP data package head structure of a network interface. The method firstly obtains the TCP/IP data package from the network interface, analyzes the option domain in the data package head structure to obtain a network connection feature vector, sends the feature vector into the fuzzy neural network, trains the fuzzy neural network by a self-adapting fuzzy neural inference system to form a model of the fuzzy neural network, subsequently, sends the feature vector into the model of trained fuzzy neural network to get an output value, and finally performs fuzzy clustering onto the output value so as to distinguish whether the output data has covert channel. The invention is applicable for a mainstream operating system, has wide application range, can increase the security of the operating system and effectively prevents secret information leakage.

A grammatical inference system for inferring a grammar from a plurality of example sentences. The system selects sentences having a common suffix or prefix component; identifies the other of said suffix or prefix component of each selected sentence; generating rules for generating the example sentences and the other components; reduces the right hand side of each rule on the basis of the right hand sides of the other rules; and generates a grammar on the basis of the reduced rules.

A method, system and computer readable medium for generating a cognitive insight comprising: receiving training data, the training data being based upon interactions between a user and a cognitive learning and inference system; performing a ranked insight machine learning operation on the training data; generating a cognitive profile based upon the information generated by performing the ranked insight machine learning operations; and, generating a cognitive insight based upon the cognitive profile generated using the plurality of machine learning operations.

The present invention provides a system and a method to keep track of conflicts resulting from combining basic probability assignments (BPAs) without actually performing the normalization operation at every step of combination. This approach can be applied recursively, allowing the system to keep track of the normalization and conflict of the belief combinations inside a large belief inference system without ever performing a normalization operation as long as the computing system precision allows. The present invention can be applied to monitor conflicts within most inference systems, including the Valuation Based System (VBS) framework for Dempster-Shafer (D-S) evidential reasoning.

The invention relates to an online diagnosis method for a linear control system network. The method comprises the steps that first, a model including two adaptive neural fuzzy inference systems is established, linear control system network state parameters obtained in a fault environment are used as sample data input, a mixed learning algorithm is used for conducting offline training on the two adaptive neural fuzzy inference systems, fuzzy control rules are extracted respectively, then the fuzzy control rules are converted into a loop-up table, a network health degree calculation function and a network fault reason judging function are generated and are added into a controller node of a linear control system, an linear control system online diagnosis algorithm based on the adaptive neural fuzzy inference systems is formed, and online diagnosis is conducted on the network health degree and the fault reason of the linear control system.

The invention discloses a D-S evidence theory-based high-voltage circuit breaker mechanical fault diagnosis method.. The method comprises the steps of establishing a self-adaptive neural fuzzy inference system model; acquiring a sound signal and a vibration signal of the high-voltage circuit breaker; extracting characteristic quantities of the sound signal and the vibration signal; performing classification preprocessing on the characteristic quantity through the adaptive neural fuzzy inference system model to obtain a basic probability distribution function; enabling a D-S evidence theory modified by high conflict evidence to fuse a basic probability distribution function to obtain a mechanical fault diagnosis result.. According to the diagnosis method provided by the invention, the soundand the vibration signal are combined. The D-S evidence theory is used for information fusion to comprehensively diagnose the mechanical failure of the high-voltage circuit breaker, which improves the reliability and accuracy of the state assessment.

The invention provides a method for obtaining race specific loci and a race inference system and application thereof. The method comprises the following steps: selecting black, white and yellow race groups; obtaining SNP loci in genomes of the black, white and yellow race groups; selecting the SNP loci in pigmentation genes and xenobiotic metabolic enzyme genes of the black, white and yellow racegroups; screening the ancestor information marker loci of the black, white and yellow race groups from the selected SNP loci; and screening the specific loci of the black, white and yellow races fromthe ancestor information marker loci. The screened specific loci of the black, white and yellow races are integrated, thus obtaining the black, white and yellow race inference system which can be used for carrying out black, white and yellow race origin inference of the individual with unknown origin accurately and efficiently.

The invention relates to a flight inference system and a method thereof; wherein, the flight inference system comprises an inference module and a display module; the inference module is used to obtain flight object collection and airspace restriction condition object collection within the scope of inference time, infer flight movement at each inference time according to the flight object collection, set monitoring state at each inference time according to the airspace restriction condition object and flight object collection and send the flight movement and monitoring state to a display module; the flight movement comprises flight number, flight position information, flight course information and flight state; the monitoring state comprises airspace alarming state and flight alarming state; the display module is used to display movement and monitoring state of the flight. Based on the flight object collection and airspace restriction condition object collection, the invention carries out inference and imitation on the flight scheduler, can effectively evaluate the formulated flight scheduler, thus ensuring scientific and rational flight scheduler, easing airspace traffic pressure and assisting work of the air traffic controller.

The invention discloses an air gap eccentricity fault diagnosis and classification method of an ANFIS wind power double-fed asynchronous motor, belonging to the field of motor state detection and fault diagnosis. Wind power double-fed asynchronous motor air gap eccentricity faults are divided into several frequent fault types, based on software simulation. A double-fed asynchronous motor model is simulated and several fault types when an air gap eccentricity happens are simulated. The changes of current in a stator winding under different eccentricities of moving and static eccentric faults, a time domain is converted into a spectrogram when the wavelet decomposition of the current is carried out, characteristic frequency bands when different faults happen are extracted, the fault characteristic frequencies corresponding to different types of the air gap eccentricity faults are analyzed, then the wavelet energy of the bands are used as training sample data, an adaptive neural fuzzy inference system for the double-fed asynchronous motor air gap eccentricity faults is constructed, a hybrid learning algorithm is introduced to carry out training, and the air gap eccentricity fault type of the double-fed asynchronous motor is judged. The method has the advantages of high precision and high operability.