126 results about "Cognitive model" patented technology

A system and method are provided for receiving speech and / or non-speech communications of natural language questions and / or commands and executing the questions and / or commands. The invention provides a conversational human-machine interface that includes a conversational speech analyzer, a general cognitive model, an environmental model, and a personalized cognitive model to determine context, domain knowledge, and invoke prior information to interpret a spoken utterance or a received non-spoken message. The system and method creates, stores and uses extensive personal profile information for each user, thereby improving the reliability of determining the context of the speech or non-speech communication and presenting the expected results for a particular question or command.

A system and method are provided for receiving speech and / or non-speech communications of natural language questions and / or commands and executing the questions and / or commands. The invention provides a conversational human-machine interface that includes a conversational speech analyzer, a general cognitive model, an environmental model, and a personalized cognitive model to determine context, domain knowledge, and invoke prior information to interpret a spoken utterance or a received non-spoken message. The system and method creates, stores and uses extensive personal profile information for each user, thereby improving the reliability of determining the context of the speech or non-speech communication and presenting the expected results for a particular question or command.

A method of operating a hearing instrument for processing an input sound and to provide an output stimulus according to a user's particular needs, and related system, computer readable medium and data processing system. An object is to provide an improved customization of a hearing instrument. The method includes the steps a) providing an estimate of the present cognitive load of the user; b) providing processing of an input signal originating from the input sound according to a user's particular needs; and c) adapting the processing in dependence of the estimate the present cognitive load of the user. The estimate of the present cognitive load of a user is produced by in-situ direct measures of cognitive load (e.g. based on EEG-measurements, body temperature, etc.) or by an on-line cognitive model in the hearing aid system whose parameters have been preferably adjusted to fit to the individual user.

A multimodal user interface is provided. A human machine interface in a vehicle utilizes a plurality of modalities. A cognitive model for secondary driving tasks indicates a best use of one or more particular modalities for performing each secondary driving task.

The invention discloses a children cognitive system based on augment reality technology and a cognitive method. The children cognitive system based on the augment reality technology comprises an image information input module, an image information matching module, a drawing model module and a voice recognition module. A set of recognition identifications are developed through interfaces of an ARToolkit augment reality development kit, a Microsoft Speech SDK voice recognition engine, a 3D max modeling tool and the like, and a children cognitive platform performs simple interaction with a virtual scene in a computer through voice recognition. Operations like moving, amplifying and contracting a model are achieved through OpenGL image processing technology and 3D modeling technology. The children cognitive system based on augment reality technology and the cognitive method have the advantages of being short in development cycle, good in maintenance, good in portability and easy to modify. In addition, a user can use the cognitive system based on augment reality technology and the cognitive method to write a literary handbook, good learning effect is achieved, and the cognitive platform which is strong in interaction is provided for a child to use an augment reality application system.

A machine-learning engine is disclosed that is configured to recognize and learn behaviors, as well as to identify and distinguish between normal and abnormal behavior within a scene, by analyzing movements and / or activities (or absence of such) over time. The machine-learning engine may be configured to evaluate a sequence of primitive events and associated kinematic data generated for an object depicted in a sequence of video frames and a related vector representation. The vector representation is generated from a primitive event symbol stream and a phase space symbol stream, and the streams describe actions of the objects depicted in the sequence of video frames.

The invention discloses a driving trajectory predicting system integrating a kinematic model and a behavioral cognition model. The driving trajectory predicting system is characterized in that an interactive mixing module subjects the prediction result of each prediction module at a last time moment to interactive mixing to output a mixed result used for prediction at a next time moment; the prediction module comprises a behavioral cognition trajectory prediction module based on behavioral cognition and a motion trajectory prediction module based on kinematics; the behavioral cognition trajectory prediction module and the motion trajectory prediction module performs prediction according to the mixed result output by the interactive mixing module and output respective prediction results including a vehicle position and a covariance matrix; a fusion updating module fuses a final prediction result according to the prediction results, updates a weight coefficient, and outputs a vehicle position and a covariance matrix at a certain time moment in the future. The driving trajectory predicting system can continuously estimate the position state and the driving behavior of the vehicle in a vehicle driving process, predicts a driving trajectory, and provides assistance for intelligent driving decision.

A process and system for providing objective quality measurement of a target audio signal. Reference and target signals are processed by a peripheral ear processor, and compared to provide a basilar degradation signal. A cognitive processor employing a neural network then determines an objective quality measure from the basilar degradation signal by calculating certain key cognitive model components.

A non-supervision clustering-based distributed cooperative spectrum sensing method for a cognitive self-organizing network relates to the field of cognitive radio in wireless communication technology. Aiming at solving the problems of difficult distributed cooperation of the cognitive self-organizing network and large overhead of whole-network cooperation, the method adopts the latest achievements of a non-supervision clustering theory and a co-recognition theory to achieve fully-distributed, steady and reliable distributed cooperative spectrum sensing under the condition of simplifying the overhead of network sensing; and users with potential optimal detection performance spontaneously gather only via information interaction between neighbors, further the users carry out cooperation spectrum sensing by utilizing an average co-recognition protocol, and a sensing result is broadcast to the whole network users. The method does not require local users to receive apriori information of noise-signal ratio, and does not need any central controllers, thereby greatly lowering sensing overhead and acquiring detection performances similar to optimal soft combination solution.

The invention discloses a student modeling and personalized course recommendation method in an online learning system, and belongs to the education data mining field. According to the method, cognitive level modeling and personalized course recommendation of students are mainly studied, firstly, knowledge mastering states of the students are judged based on a cognitive diagnosis model, learning behaviors of the students are analyzed through data on a system platform, and then the cognitive abilities of the students are modeled by integrating course mastering conditions; secondly, an online course is modeled ; and finally, the features of the online course are fused according to the cognitive level of the student to perform personalized recommendation. According to the invention, personalized recommendation is carried out based on the cognitive level of students and in combination with the feature indexes of the online courses, so that the user can be helped to carry out more accurate personalized course recommendation, and the online course recommendation is more interpretable and acceptable.

A cognitive method, system and computer readable media comprising: monitoring a user interaction of a user; generating user interaction data based upon the user interaction; receiving data from a plurality of data sources; processing the user interaction data and the data from the plurality of data sources to perform a cognitive learning operation, the processing being performed via a cognitive inference and learning system, the cognitive learning operation comprising analyzing the user interaction data, the cognitive learning operation generating a cognitive learning result based upon the user interaction data; associating a cognitive profile with the user based on the cognitive learning result; and, performing a cognitive personal assistant operation based upon the cognitive profile, the cognitive personal assistant operation assisting the user by performing a personal assistant task.

A construction method of a personalized learning feature model based on a knowledge graph comprises the steps: constructing a course-oriented knowledge graph, wherein entities correspond to knowledgepoints and learning resources; describing a relationship between knowledge point entities by adopting a directed acyclic graph; describing and learning a relationship between resource entities by utilizing a Bloom cognitive hierarchy; constructing a student personalized cognitive model, and adopting transfer learning to solve the problem of scarcity of personalized information of a single student;adopting active learning to solve the value evaluation problem of the selected data; and constructing a dynamically evolved student personalized cognitive model by utilizing a Markov chain theory. The invention discloses a construction method of a personalized learning feature model based on a knowledge graph. By comprehensively utilizing the knowledge graph, the graph theory, the Markov chain, the Bloom cognitive hierarchy theory and other methods, the personalized learning characteristics of the students are effectively positioned, the accuracy of positioning the personalized characteristics of the students is improved, and therefore quantitative decision support is provided for improving the learning efficiency and the teaching effect.

The invention discloses a spectrum detection method of cognitive radio system, comprising the following steps: calculating the signal energy of each cognitive node; performing spectrum detection judgment on each cognitive node; and carrying out united judging on the spectrum detection of a centre node. By the method disclosed by the invention, the frequency spectrum detection is performed on the centre node via performing the spectrum detection on each cognitive node and using the signal energy value of each cognitive node; when an authorized user signal is detected under quite low signal to noise ratio, simultaneously, whether an authorized user receiver normally receives and demodulates or not also can be detected. Therefore, during practical application, the cognitive radio system can improve the frequency spectrum utilization better under the condition of ensuring the detection performance and without interfering the normal receiving of the authorized user.

The invention belongs to the technical field of emergency plan management and relates to a method for generating a plan based on a cognitive model. The method comprises the following steps of: inputting a bound variable by a decision maker, and calculating the level of the emergency by a system; performing characteristics matching in a case and plan base; if the matching is successful, outputting action sequences in a corresponding plan and a corresponding case, and judging the validity according to a situation; if the matching fails, returning to an initial state; if the generated initial plan decision scheme needs adjusting, performing re-matching on the case and the plan; and if the generated initial plan decision scheme does not need adjusting, obtaining a target plan decision scheme. Due to the adoption of the cognitive model and the plan CSP matching algorithm, the method has higher accuracy and real-time characteristic, and provides effective situation integration information and decision-making information for the decision maker by using the high-performance calculation power of a computer; and simultaneously, the high cognition of the decision maker on the field is fully utilized, and a comprehensive emergency plan is repeatedly studied under the intervention of the decision maker, so higher reality usability is achieved.

The invention provides power line communication equipment based on a channel cognitive technology. The equipment comprises: a power line communication transceiver, which has a configurable parameter; a channel cognitive module, which is used for carrying out cognition on a power line channel and obtaining a channel model; and a cognitive control module, which is used for carrying out learning according to the channel model obtained by the channel cognitive model so as to determine and configure a configuration parameter of the communication transceiver in the communication equipment. Compared with the prior art, the technology employed in the invention enables a configuration parameter of a communication transceiver in the power line communication equipment to be configured, so that a whole power line communication system can work in the most suitable state and thus reliability, stability and a communication speed of the power line communication are improved; meanwhile, a frequency spectrum utilization rate and a power consumption utilization rate can be improved.

Digital technology utilizing a cognitive model based on human naturalistic decision-making processes, including pattern recognition and episodic memory, can reduce the dependency of human-machine interactions on the abilities of a human user and can enable a machine to more closely emulate human-like responses. Such a cognitive model can enable digital technology to use cognitive capacities fundamental to human-like communication and cooperation to interact with humans.

The invention discloses a video multi-scale visualization method and a video multi-scale interaction method. The video multi-scale interaction method comprises the steps of establishing a user cognition model, which is oriented to a video content structure, of a target video; extracting a foreground object and a background scene in the target video and an image frame of the foreground object; obtaining a moving target and a corresponding trajectory thereof; calculating an appearance density of the moving target according to a time shaft-based moving target appearance amount and a correspondingtime mapping relation; extracting a key frame from processed target video data, and performing annotation on moving target information in the key frame; performing multi-scale division on a processedmoving target identification result and trajectory data of the moving target to generate a multi-scale video information representation structure; and introducing a sketch interactive gesture to an interactive interface of the multi-scale video information representation structure in combination with corresponding semantics of a mouse interactive operation based on an interactive operation mode of a user in an interactive process, and operating the target video on the interactive interface through the sketch interactive gesture.

A training system and method for measuring, evaluating, and improving the functional efficiency of information processing stages associated with human cognition. Scene dependent and a-priori queries can be created as a part of a training curriculum and an appropriate measuring technique can be executed with respect to each scene to measure the functional efficiency of the cognitive information processing stages. Thereafter, a trainee's response with respect to such queries can be automatically evaluated. Feedback based on the response can then be generated. An adaptive training mechanism can be configured to enhance the functional efficiency of the cognitive information processing stages.

The invention discloses a vibration distortion-based voice frequency objective quality evaluating method and system. The system comprises a sensing model and a cognition module, wherein the sensing module further comprises a time frequency analyzing module, a tone sensing module, a quality vibration module and an energy balancing module; the output of the sensing module is model output parameters and is fitted through the cognition module so as to obtain user experience scores. The invention provides a method and a system for evaluating objective quality of voice frequency, which consider quality vibration influence, the method and the system are applicable to mobile voice frequency of middle-low code rate voice frequency, and the evaluation result is more approximate to real subjective hearing feeling.

The present design is directed to a system for defuzzification of multiple qualitative signals into human-centric threat notifications using cognitive computing techniques, including a series of periodic execution components configured to operate over full or partial sets of received data, and a plurality of event reception components configured to operate on each event in a stream relevant to a terrain and selectively provide information to the series of periodic execution components. The system detects and signals anomalies upon detecting behavior inconsistent with normal behavior.

The invention relates to a bionic intelligent control method. In order to solve the problems that the intelligence level capable of being met by a traditional robot control method is limited, a robot cannot independently adapt to an unknown environment, has difficulty in acquiring the capacity for completing complex tasks from simple experience and cannot complete tasks through self-learning, a biological sensorimotor nervous system is simulated from the bionic perspective, and the operant conditioning mechanism is fused into the design of the sensorimotor system. Biological motion neurocognition is reproduced by copying the sensorimotor system, simulation the biological cognitive mechanism is facilitated, and thus the cognitive level of the robot is improved; the operant conditioning function is added, and thus the feedback closed loop relation of interaction between perception and motion in the sensorimotor system is explained, the system is made to show biologically similar self-learning behaviors, and the intelligence level of the robot is improved.

The invention discloses an iterative cooperative spectrum sensing method, which aims to solve the problem that time, energy and spectrum are greatly consumed in transmission and judgment processes when the number of cognitive users is over-great in the conventional cognitive radio spectrum sensing process. In the method, authorized users are judged whether to be in an active state or not by iterative sensing according to judgment results of a judgment center at past moments and the judgment result of a cognitive node at a current moment so as to realize high sensing probability by the two cognitive users; the iterative sensing improves the sensing performance of a system, and gradually increases the sensing probability as time goes by; and only one cognitive user senses the authorized users at each sensing moment except a first moment, so the number of the used cognitive users can be saved by a half.

The invention relates to a shaft furnace condition fault forecasting method based on improved case-based reasoning. An attribute weight allocation model is added on the basis of a 4R cognitive model, and the GDM (global data manager) theory is utilized to improve a case-correcting model. The shaft furnace fault condition forecasting method includes initializing variables, normalizing current variables to keep values ranging from 0 to 1; displaying a case, establishing a case base; calculating correlation coefficients based on the water filling weight allocating algorithm, calculating case attribute weight; calculating similarity between target cases and source cases, confirming numbers of matching cases according to a similarity threshold value; judging reusing effect; performing GDM calibration to forecast results; storing the corresponding cases, and outputting operation guides. The shaft furnace condition fault forecast based on improved case-based reasoning is realized by utilizing online process data. Compared with a method for judging furnace condition manually, the shaft furnace condition fault forecasting method based on improved case-based reasoning reduces workload of operators, reduces uncertainty of manual judgment and increases timeliness of fault forecast.

The present design is directed to a cognitive system including a receiver configured to receive a set of actors and associated actor information and receive assets and their associated asset information, a creation apparatus configured to create data dictionary entries for a taxonomy based on the set of actors and the assets and create a cognitive model using the data dictionary entries for a time period, and a computing apparatus configured to compute trust of the cognitive model as a fuzzy number and activate the cognitive model if trust of the cognitive model is above a cognitive model trust threshold. When the cognitive model is activated, the cognitive modeling system is configured to schedule a collection of tasks to run that perform regular extraction of actions from an original data source and perform at least one anomaly analysis associated with the cognitive model.

The invention discloses an effectiveness verification method for interactive system usability design. The effectiveness verification method for the interactive system usability design includes steps that modeling, to be specific, building a cognitive model, a task model and a system usability design model, and using IMPA process algebra to uniformly build models of an interactive system; stipulating an agreement, to be specific, building a usability agreement of the interactive system, wherein the usability comprises safety and reliability; verifying, using an IMC model verifier to verify the safety and reliability design effectiveness of the interactive system. The effectiveness verification method for the interactive system usability design is capable of forecasting safety disadvantages due to close human error, evaluating the reliability and cognitive complexity of a post-WIMP interface and quantitatively comparing the efficiency of a WIMP interface with the efficiency of the post-WIMP interface.

The present design is directed to a system for measuring values qualitatively across a terrain including multiple dimensions using cognitive computing techniques, comprising a plurality of event reception components configured to operate on each event in a stream relevant to the terrain, the plurality of event reception components including a threshold application component configured to apply a threshold to each element in the stream, a terrain updater configured to update the terrain based on at least one event, an outlier analysis module configured to determine any outlier in the stream of events, a threshold violation predictor configured to predict threshold violations based on the stream of events, a time-ordered behavior evaluator configured to evaluate behavior based on the stream of events, and a graph updater to update a graph based on the stream of events.