1520 results about "Online learning" patented technology

An interactive voice actuated control system for a testing machine such as a tensile testing machine is described. Voice commands are passed through a user-command predictor and integrated with a graphical user interface control panel to allow hands-free operation. The user-command predictor learns operator command patterns on-line and predicts the most likely next action. It assists less experienced operators by recommending the next command, and it adds robustness to the voice command interpreter by verbally asking the operator to repeat unlikely commanded actions. The voice actuated control system applies to industrial machines whose normal operation is characterized by a nonrandom series of commands.

Disclosed herein is a computer implemented method and system for enabling real-time synchronous group interactions among participants in a virtual discussion forum. The virtual discussion forum may be a web seminar, an online conference session, an online debate, an online learning session, etc. A virtual interaction platform comprising a synchronous media layer, an interaction layer, and a reporting, archiving, and analysis layer is provided to the participants. The virtual interaction platform enables synchronous group interactions among the participants by introducing discussion issues for the virtual discussion forum. Media content related to the discussion issues is synchronously rendered to the participants. The virtual interaction platform enables the participants to interact with each other and respond to questions, polls, etc. posted in the virtual discussion forum. The virtual interaction platform determines group behavior by analyzing participants' responses and interactions and displays participants' group behavior by visually representing individual and group traits.

A system and method for authors of on-line learning material and learning objects is provided to develop and store their learning objects while also protecting their digital rights during the marketing and distribution of the learning objects. The digital content may include such asset content as video, music, text, educational content, or the like. Various stages of the invention include: (i) author registration that permits authors to access and become a registered user of the system, (ii) author content creation that permits authors to compose and create a Shareable Content Object (SCO) which represents one or more assets, (iii) DRM packaging that permits name tagging and security encasement, and (iv) ingesting and eStore publishing.

A system and method for adaptive online learning management for creating and / or publishing customized, personalized, adaptive, and student-specific online course content to enable and provide an efficient learning environment to each student based on the dynamically changing self-evolving student's profile. A student's profile can be created based on factors such as demographic profile, psychographic profile, learning style, personality traits, interests, social networking profile, social media interactions, online interaction characteristics, social networking circle attributes, prerequisite knowledge assessments, social profile, skill, and performance of the student, wherein the student's profile can be processed with respect to a learning object repository to generate a defined set of student-specific learning objects that best suit the profile of the student. A online course content can accordingly be generated based on the defined set of student-specific learning objects.

The invention discloses a mobile terminal based online teaching interactive system and an implementation method thereof. The system is composed of a plurality of mobile phone client ends of a social platform, computer client ends of the social platform and a cloud platform. The method includes the steps that a teacher creates online teaching interactive resources on the cloud platform and uploads teaching resources through the cloud platform and users uploads teaching resources through the social platform; the teacher performs online interactive teaching on the users and the cloud platform records a user online interactive learning process in real time; the users achieve self-learning and the teacher achieves specific comments, analysis and counseling according to user account information of a data base of the cloud platform; the teacher achieves mastery of learning states of all of the users according to the user account information of the data base of the cloud platform. The mobile terminal based online teaching interactive system and the implementation method of the mobile terminal based online teaching interactive system have the advantages that students achieve fragmented, interesting and independent learning and the teacher achieve automatic and intelligent teaching management, thereby developing an innovative teaching mode and bringing a unique classroom experience.

The invention discloses an online learning system which comprises a courseware database module, a management module, and a student learning situation monitoring module. The student learning situation monitoring module comprises a face recognition module, a facial expression analysis module, a video scene analysis module, a sound recognition module, a monitoring video storage module, and a video acquisition module, and the like. The learning situation monitoring module can be applied to monitor the student learning and exam situations in real time, thereby preventing other people from taking learning and tests for someone, and improving the learning efficiency of students.

The invention discloses a method for predicting the future speed trajectory of a hybrid power bus. The method includes the following steps that parameters are obtained and normalized; an input parameter vector and an output parameter vector of an RBF neural network are determined; the RBF neural network is offline trained; the future speed trajectory is online predicted. Based on online learning of the RBF neural network, prediction of the speed trajectory is studied under the precondition that global optimization of the control strategy of the hybrid power bus is achieved from the aspect of a people-bus-environment system, the method for predicting the bus future driving trajectory is provided and fully takes bus state parameters, driver driving styles and front road environment and traffic state parameters into consideration, and accuracy of prediction of the bus speed is improved. The influences of the people-bus-environment system on the future working condition is fully taken into consideration, accuracy of prediction of the working condition is improved while the bus has the precognition capacity, and a good foundation is laid for global optimization of the control strategy.

A system and method of problem determination and resolution utilizes enhanced problem classification, and effectively categorizes any problem a user experiences by leveraging all available data to recognize the specific problem. Historical problem data is labeled with the cause of that problem and is analyzed to learn problem patterns. The historical problem data is classified into a predefined hierarchical structure of taxonomies by using an incremental online learning algorithm. The hierarchical structure and learned patterns are utilized to recognize problems and generate the root cause of the problem when given a new set of monitoring data and log data.

The invention discloses an online learning cognitive map generation system and method for representing the knowledge learning mastering state of a leaner in a specific field. The system comprises an intelligent terminal and a server. The intelligent terminal is used for online learning interaction. The online learning cognitive map generation system runs in the server comprising a knowledge graph construction module, a learning interaction data acquisition module, a knowledge state calculation and prediction module and a visual output module. The online learning cognitive map generation system and method are suitable for a common online learning platform and can simulate the cognitive structure of the learner and diagnose the learning defect of the leaner to provide personalized learning resources and social network services for the learner, so that learning confusion is reduced, cognitive loads are reduced, and learning pertinency and accuracy are improved.

A network system providing highly reliable transmission quality for delay-sensitive applications with online learning and cross-layer optimization is disclosed. Each protocol layer is deployed to select its own optimization strategies, and cooperates with other layers to maximize the overall utility. This framework adheres to defined layered network architecture, allows layers to determine their own protocol parameters, and exchange only limited information with other layers. The network system considers heterogeneous and dynamically changing characteristics of delay-sensitive applications and the underlying time-varying network conditions, to perform cross-layer optimization. Data units (DUs), both independently decodable DUs and interdependent DUs, are considered. The optimization considers how the cross-layer strategies selected for one DU will impact its neighboring DUs and the DUs that depend on it. While attributes of future DU and network conditions may be unknown in real-time applications, the impact of current cross-layer actions on future DUs can be characterized by a state-value function in the Markov decision process (MDP) framework. Based on the dynamic programming solution to the MDP, the network system utilizes a low-complexity cross-layer optimization algorithm using online learning for each DU transmission.

The invention relates to a method for analyzing facial expressions on the basis of motion tracking, in particular to a technique for face multi-feature tracking and expression recognition. The method comprises the following steps: pre-processing an inputted video image, and carrying out the face detection and face principle point location to determine and normalize the position of the face; modeling the face and expressions by using a three-dimensional parametric face mesh model, extracting the robust features and tracking the positions, gestures and expressions of the face in the inputted video image by combining the online learning method, so as to achieve the rapid and effective face multi-feature tracking; and taking the tracked expression parameters as the features for expression analysis; and carrying out the expression analysis by using an improved fuzzy clustering algorithm based on Gaussian distance measurement, so as to provide the fuzzy description of the expression.

The invention relates to a method which automatically classifies motion targets learning online, models an image sequence background and detects the motion targets, scene variation, coverage viewing angle and partitioning scene, extracts and clusters characteristic vectors, and marks region classes; the number of the motion targets in a sub-region and certain threshold value initialize Gaussian distribution and prior probability to accomplish initialization of a classifier in accordance with the characteristic vectors of all the motion target regions that pass through the sub-region; the motion targets in the sub-region are classified and parameters of the classifier are online iterated and optimized; classification results in the process of tracking the motion targets are synthesized to output the classification result of the motion result that learns online. The invention is used for detection of abnormalities in monitor scenes, establishing rules for various class targets, enhancing security of monitor system, identifying objects in the monitor scenes, lessening complexity of identification algorithm, improving rate of identification, and for semantized comprehension for the monitor scenes, identifying classes of the motion target and aiding to comprehension for behavior events occurring in the scenes.

The invention discloses an on-line sequential extreme learning machine-based incremental human behavior recognition method. According to the method, a human body can be captured by a video camera on the basis of an activity range of everyone. The method comprises the following steps of: (1) extracting a spatio-temporal interest point in a video by adopting a third-dimensional (3D) Harris corner point detector; (2) calculating a descriptor of the detected spatio-temporal interest point by utilizing a 3D SIFT descriptor; (3) generating a video dictionary by adopting a K-means clustering algorithm, and establishing a bag-of-words model of a video image; (4) training an on-line sequential extreme learning machine classifier by using the obtained bag-of-words model of the video image; and (5) performing human behavior recognition by utilizing the on-line sequential extreme learning machine classifier, and performing on-line learning. According to the method, an accurate human behavior recognition result can be obtained within a short training time under the condition of a few training samples, and the method is insensitive to environmental scenario changes, environmental lighting changes, detection object changes and human form changes to a certain extent.

The invention discloses a man-machine interaction method and system for online education based on artificial intelligence, and relates to the digitalized visual and acoustic technology in the field ofelectronic information. The system comprises a subsystem which can recognize the emotion of an audience and an intelligent session subsystem. Particularly, the two subsystems are combined with an online education system, thereby achieving the better presentation of the personalized teaching contents for the audience. The system starts from the improvement of the man-machine interaction vividnessof the online education. The emotion recognition subsystem judges the learning state of a user through the expression of the user when the user watches a video, and then the intelligent session subsystem carries out the machine Q&A interaction. The emotion recognition subsystem finally classifies the emotions of the audiences into seven types: angry, aversion, fear, sadness, surprise, neutrality,and happiness. The intelligent session subsystem will adjust the corresponding course content according to different emotions, and carry out the machine Q&A interaction, thereby achieving a purpose ofenabling the teacher-student interaction and feedback in the conventional class to be presented in an online mode, and enabling the online class to be more personalized.

The invention discloses a multimedia network instructional system which comprises an administrator side, a teacher side and N student sides, wherein the administrator side is used for carrying out instructional resource administration and training business administration; the teacher side is used for performing an instructional activity and carrying out a learning supporting activity; the student sides are used for carrying out course learning and communicating with a teacher and other students; the administrator side feeds back information to the teacher side or monitors the work of the teacher; the teacher side provides or correct an instructional document and an instructional resource for the administrator side; the administrator side implements a service for the student sides, and provides a course constructing module for the student sides; the student sides can mutually learn and communicate with the teacher side, and provide a course feeding-back module for the teacher side; the student sides can learn and communicate with one another, and complete a course implementing module. The multimedia network instructional system can support instructional activities of on-line learning of the students, instruction giving of the teacher and examinations, provides personalized services for users at different levels and with different demands, and provides services of learning other languages by a mother language for the users.

The invention belongs to the field of computer vision and pattern recognition and discloses a moving target tracking method based on an improved multi-example learning algorithm. Firstly, a random measurement matrix is designed according to the compression perception theory. Then a multi-example learning algorithm is used to sample an example in a current tracking result small neighborhood to form a positive package, and at the same time, sampling an example is carried out in a large neighborhood ring to obtain a negative package. For each example, the characteristic of a character target is extracted at an image surface, and the random measurement matrix is utilized to carry out dimensionality reduction on the characteristic. According to the extracted example characteristic, online learning weak classifiers are utilized, and weak classifiers with strong discrimination ability are selected from a weak classification pool to form a strong classifier. Finally, when a new target position is tracked, according to a similarity score of the current tracking result and a target template, the online adaptive adjustment of classifier update degree parameters is carried out. According to the method, a problem that a tracking result in the existing algorithm is easily affected by an illumination change, an attitude change, the interference of a complex background, target fast motion and the like is solved.

The invention discloses a self-adaptive cascade classifier training method based on online learning, which comprises the following steps: (1), preparing a training sample set with a small quantity of samples, and training an initial cascade classifier HC(x) in a cascade classifier algorithm; (2), using the HC(x) for traversal of image frames to be detected, extracting areas with sizes identical to the sizes of the training samples one by one, calculating a feature value set, classifying the areas with the initial cascade classifier, and judging whether the areas are target areas, thereby completing target detection; (3) tracking the detected targets in a particle filtering algorithm, verifying the target detection results through tracking, marking detection with errors as a negative sample for online learning, obtaining different attitudes of real targets through tracking and extracting a positive sample for online learning; and (4) carrying out online training and updating for the initial cascade classifier HC(x) in a self-adaptive cascade classifier algorithm when an online learning sample is obtained, thereby gradually improving the target detection accuracy of the classifier.

The invention discloses a real-time dense monocular simultaneous localization and mapping (SLAM) method based on an online learning depth prediction network. The method comprises: optimization of a luminosity error of a minimized high gradient point is carried out to obtain a camera attitude of a key frame and the depth of the high gradient point is predicted by using a trigonometric survey methodto obtain a semi-dense map of a current frame; an online training image pair is selected, on-line training and updating of a CNN network model are carried out by using a block-by-block stochastic gradient descent method, and depth prediction is carried out on the current frame of picture by using the trained CNN network model to obtain a dense map; depth scale regression is carried out based on the semi-dense map of the current frame and the predicted dense map to obtain an absolute scale factor of depth information of the current frame; and with an NCC score voting method, all pixel depth prediction values of the current frame are selected based on two kinds of projection results to obtain a predicted depth map, and Gaussian fusion is carried out on the predicted depth map to obtain a final depth map. In addition, the invention also provides a corresponding real-time dense monocular SLAM system based on an online learning depth prediction network.

An online learning behavior analysis-based individualized learning recommending method comprises the following steps of 1 collecting the data, and obtaining the learner log data, the student score data and the student character data of an online learning platform; 2 extracting and mining the features; 3 preparing a dataset to train a BP neural network and testing the accuracy of a model; 4 utilizing the trained model to predict the scores of the new learners; 5 issuing the questionnaires to the learners, and collecting the questionnaire data; 6 carrying out the k-means clustering analysis on the questionnaire data; 7 combining the character features of the different learners to recommend the individualized learning methods. The present invention provides a BP neural network prediction method that is higher in prediction precision and can analyze the online learning time regularity of the learners more comprehensively. According to the present invention, the characters of the learners can be analyzed, and the individualized learning methods can be recommended to the learners.

Hot-electron injection driven by hole impact ionization in the channel-to-drain junction of a p-channel MOSFET provides a new mechanism for writing a floating-gate memory. Various pFET floating-gate structures use a combination of this mechanism and electron tunneling to implement nonvolatile analog memory, nonvolatile digital memory, or on-line learning in silicon. The memory is nonvolatile because the devices use electrically isolated floating gates to store electronic charge. The devices enable on-line learning because the electron injection and tunneling mechanisms that write the memory can occur during normal device operation. The memory updates and learning are bidirectional because the injection and tunneling mechanisms add and remove electrons from the floating gate, respectively. Because the memory updates depend on both the stored memory and the pFETs terminal voltages, and because they are bidirectional, the devices can implement on-line learning functions.

The invention discloses an image marking method based on multi-mode deep learning. The method comprises the following steps: firstly, a depth neural network is trained by utilization of images without labels; secondly, each single mode is optimized by utilization of counter propagation; finally, weights among different modes are optimized by utilization of on-line learning power gradient algorithm. The method employs a convolution neural network technology to optimize parameters of the depth neural network, and the marking precision is raised. Experiments of public data sets show that the method can raise the image marking performance effectively.

The invention discloses a four-rotor aircraft control method based on PID neural network (PIDNN) control. First, dynamic equations of a four-rotor aircraft are established based on the Newton-Euler equations, and then, a nested controller is proposed. An inner loop decentralized controller is designed based on a PIDNN method to realize attitude control, and the outer loop adopts a classical PID control method. Next, online learning of a PIDNN controller is realized by an error back propagation method, the connection initial weight of PIDNN is determined based on the principle of PID control, and an appropriate learning step is selected according to the discrete Lyapunov theory to guarantee the convergence of the controller. Thus, the control problem of a four-rotor aircraft caused by high degree of nonlinearity, strong coupling and under-actuating capability, disturbance from the external environment in the process of flight and other factors can be effectively solved.

The invention discloses a target tracking method which is applied to electronic equipment. The electronic equipment is provided with an image acquisition unit which is used for acquiring image data. The method comprises the steps that a tracking target is determined in the initial frame of image of the image data; multiple candidate targets are extracted in the subsequent frame of image of the image data, wherein the subsequent frame of image is any frame of image after the initial frame of image; the similarity of each candidate target and the tracking target is calculated; and the candidate target having the highest similarity with the tracking target in the multiple candidate targets is determined to be the tracking target. The technical problems in the prior art that an online learning visual tracking method cannot judge lost of the tracking target and the tracking target is difficult to find after lost of tracking target can be solved. Meanwhile, the invention also discloses the electronic equipment.

The invention discloses a dialog strategy online realization method based on multi-task learning. According to the method, corpus information of a man-machine dialog is acquired in real time, current user state features and user action features are extracted, and construction is performed to obtain training input; then a single accumulated reward value in a dialog strategy learning process is split into a dialog round number reward value and a dialog success reward value to serve as training annotations, and two different value models are optimized at the same time through the multi-task learning technology in an online training process; and finally the two reward values are merged, and a dialog strategy is updated. Through the method, a learning reinforcement framework is adopted, dialog strategy optimization is performed through online learning, it is not needed to manually design rules and strategies according to domains, and the method can adapt to domain information structures with different degrees of complexity and data of different scales; and an original optimal single accumulated reward value task is split, simultaneous optimization is performed by use of multi-task learning, therefore, a better network structure is learned, and the variance in the training process is lowered.

A method and system for on-line learning of landmark detection models for end-user specific diagnostic image reading is disclosed. A selection of a landmark to be detected in a 3D medical image is received. A current landmark detection result for the selected landmark in the 3D medical image is determined by automatically detecting the selected landmark in the 3D medical image using a stored landmark detection model corresponding to the selected landmark or by receiving a manual annotation of the selected landmark in the 3D medical image. The stored landmark detection model corresponding to the selected landmark is then updated based on the current landmark detection result for the selected landmark in the 3D medical image. The landmark selected in the 3D medical image can be a set of landmarks defining a custom view of the 3D medical image.

The invention particularly relates to an audio / video combined monitoring method and a system thereof, belongs to the technical field of industrial environment monitoring, and aims to solve the problems in the prior art, for example, the monitoring is only conducted by video monitoring operators on duty, who are easy to be fatigue and hardly identify places with potential safety hazards, moreover, video monitoring is limited by functions and viewing angles, so that the potential hazard cannot be found in time, thereby missing rescue opportunities. The audio / video combined monitoring method uses audio signals and video signals at the same time to conduct environmental monitoring, and guides the operators on duty to selectively observe video windows by using the identification results of the audio signals. The processing method of the audio signals comprises the following steps: (1) feature extraction, (2) model training, (3) sound classification, (4) online study, and (5) hazard rating evaluation.

The invention belongs to the technical field of vehicle intelligent aided driving and vehicle safety, and particularly relates to a self-adaptive cruise system which changes system self-adaptive cruise vehicle following performance by learning driver style behaviors online and has driving style learning capacity and an implementation method. The self-adaptive cruise system with the driving style learning capacity and the implementation method are designed for solving vehicle self-adaptive cruise control problems. The system is applicable to L2 automatic drive vehicles, and aims at achieving the purpose of self-adaptively following a vehicle in front in an actual driving scene through environment perception and information fusion; meanwhile, driving styles of specific drivers are consideredin a driving strategy of the system, so that the system has consistent satisfaction degree and acceptability for different people. To achieve the purpose, a mode of combining a linear quadratic typecontrol method and a reinforcement learning online learning method is adopted for better optimizing the system performance.

The invention discloses an attention detection method with the combination of sight detection. The method includes face detection, eye closing detection, eye sight detection and head deflection detection, images are captured through cameras, and eye closing characteristics, head deflection characteristics and sight characteristics of students in each frame are analyzed to determine whether the attention of the students is focused. According to the attention detection method, with the combination of the sight detection method, the problems that the students are in head emmetropia, eyes are open, and the sight is in an area outside a computer screen are solved, the concentration condition of the attention of the students during online learning can be detected, the method is advantaged by high accuracy, good stability and high reliability, and the method is scientific and practical.