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819 results about "Online learning" patented technology

Mobile terminal based online teaching interactive system and implementation method thereof

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.

Moving target classification method based on on-line study

InactiveCN101389004AAutomatic judgmentAlgorithms are efficientImage analysisClosed circuit television systemsClassification methodsImage sequence
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.

Man-machine interaction method and system for online education based on artificial intelligence

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.

Multimedia network instructional system

InactiveCN106157715AModify personal contact informationChange login passwordElectrical appliancesLesson studyOnline learning
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.

Moving target tracking method based on improved multi-example learning algorithm

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.

Real-time dense monocular SLAM method and system based on online learning depth prediction network

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.

Video human face identification and retrieval method based on on-line learning and Bayesian inference

The invention discloses a method for recognizing and retrieving video faces based on on-line study and Bayesian inference. The method comprises the following steps: step one: establishing an initialization model of a face recognition model, (i.e. the face recognition model adopts a GMM face recognition model); step two: establishing a face category model, (i.e. the model renewal of the initialization face model is performed by adopting an incremental learning manner); step three: recognizing and retrieving video faces. The test sequence and the category model are assigned, the sequence recognition information of the accumulation video in the Bayesian inference process is utilized, the probability density function of the identity is propagated according to information of a time axis, and the method provides recognition results of the video faces for users based on the MAP rules to obtain recognition scores. The invention establishes a model training frame based on non-supervised learning completely, according to spatial distribution of the training sequence, the initialization model is evolved for the category model in different modes, and the distribution of spatial data is better fitted through adjusting Gaussian mixture number of the face category model.

Medical image aided diagnosis and semi-supervised sample generation system

The invention relates to the technical field of medical image computer aided diagnosis, and discloses a medical image auxiliary diagnosis and semi-supervised sample generation system. The medical image aided diagnosis and semi-supervised sample generation system comprises a hospital image filing and communication module (a), a pulmonary nodule-based automatic detection module (b), a deep learning-based semantic annotation generation module (c) and a sample library module (d), wherein the hospital image filing and communication module is used for filing image data; the pulmonary nodule-based automatic detection module is used for automatically detecting pulmonary nodules to generate suspected pulmonary nodule positions; the deep learning-based semantic annotation generation module is used for receiving a small quantity of instructions of doctors and generating samples with accurate pulmonary nodule profiles and pulmonary nodule ingredient properties; and the sample library module is used for storing qualified samples so that online learning can be carried out by other self-learning systems. According to the system, the problem that computer aided software cannot feed closed loops back, cannot generate high-quality samples and cannot carry out self-learning is solved, and a feasible method is provided for a self-learning computer aided diagnosis system of CT images.

Online teaching platform based on big data and method for managing online teaching process through such online teaching platform based on big data

The invention discloses an online teaching platform based on big data and a method for managing an online teaching process through such online teaching platform based on big data, belonging to the technical field of teaching management. The online teaching platform comprises a teaching system, a teaching affairs management system and a big data analysis system, the teaching system is in bidirectional connection with the teaching affairs management system through a HTTP, and is used for bidirectional synchronization of teaching affairs related data, such as students and course basic data and course selection information; the teaching system is connected with the big data analysis system, and is used for acquiring structured, semi-structured and non-structured data and storing the acquired data in the big data analysis system; and the big data analysis system is respectively connected with the teaching system and the teaching affair management system, and is used for feeding back learning behavior features and teaching quality analysis results to the teaching system and the teaching affairs management system. The platform and the method provided by the invention can provide full lifecircle learning and management services, such as student enrolling, online learning, teaching management and teaching quality evaluation, through an online one-stop mode, and can analyze and evaluatelearning and teaching quality and optimize learning experience.
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