30 results about "Human learning" patented technology

The invention relates to a method and means of grading students. This invention is a software application utilizing profiling techniques that when combined or imbedded with available knowledge on the human learning experience will individually evaluate each student based on their current ability and capacity to learn, by subject. Student's tests are graded electronically against their personal ability and capability profile for grade. The profile system software is applicable to many learning situations requiring a measured outcome.

The present invention comprises methods and systems for enhancing learning skills and development of cognitive functions in humans. The methods comprise a defined set of steps or modules that include cognitive function enhancement activities such as enhancing memory and pattern recognition, and to build neuronal pathways necessary for overall cognitive improvement.

A knowledge management system to optimize the human learning and memorization process. Memorization of desired information is achieved through knowledge transfer and rehearsal, whereby a learning system provides a series of associated elements to the user using a dynamical and reactive algorithm that responds to the user's abilities to prepare an efficient rehearsal process. The instant system combines a dynamic learning system with a database that comprises content provided by multiple users and which is overseen by individuals to ensure the validity of the content within the system. Information may be provided in a multimedia format which can include music, video, text and other auditory and visual stimuli as part of the rehearsal process.

The AKOS (Artificial Knowledge Object System) of the invention is a software processing engine that relates incoming information to pre-existing stored knowledge in the form of a world model and, through a process analogous to human learning and comprehension, updates or extends the knowledge contained in the model, based on the content of the new information. Incoming information can come from sensors, computer to computer communication, or natural human language in the form of text messages. The software creates as an output. Intelligent action is defined as an output to the real-world accompanied by an alteration to the internal world model which accurately reflects an expected, specified outcome from the action. These actions may be control signals across any standard electronic computer interface or may be direct communications to a human in natural language.

The invention discloses a few-sample image sentiment classification method based on meta-learning, and the method comprises the steps: firstly constructing a plurality of meta-learning tasks similar to few-sample images with sentiment label information in a target data set on a source data set, obtaining a good classification model initialization parameter through the learning of the meta-learning tasks, so as to enable the classification model to obtain a better classification effect when facing emotion images in a few-sample target data set. According to the method, the need for annotation data can be greatly relieved, the mode based on meta-learning better conforms to the human learning mode (new human learning tasks are learned based on learned tasks), and the neural network model can be more intelligent.

The invention provides a construction method of a multi-task classification network based on an orthogonal loss function. The constructed multi-task classification network simulates a human learning process, a deep convolutional neural network is used as a hidden layer to simulate the brain of a human to perform deep feature extraction, a tree classifier is used as a task-related output layer to perform progressive classification, and the recognition process forms different learning tasks Characteristics obtained by different tasks to better meet respective requirements. When the classifier completes a coarse classification task, the depth features of the same coarse class are more aggregated; and when the fine classification task is completed, the depth features of different fine classesare more discrete, and the task output layer features of different classification tasks are distinguished, so that classifiers of different hierarchies can obtain features better matched with different classification tasks, useless features are removed, and the classification accuracy is improved.

The AKOS (Artificial Knowledge Object System) of the invention is a software processing engine that relates incoming information to pre-existing stored knowledge in the form of a world model and, through a process analogous to human learning and comprehension, updates or extends the knowledge contained in the model, based on the content of the new information. Incoming information can come from sensors, computer to computer communication, or natural human language in the form of text messages. The software creates as an output. Intelligent action is defined as an output to the real-world accompanied by an alteration to the internal world model which accurately reflects an expected, specified outcome from the action. These actions may be control signals across any standard electronic computer interface or may be direct communications to a human in natural language.

Methods and systems for training a broad population of learners in the field of robotics and automation technology based on physical interactions with a robotic training system are described herein. Specifically, a robotic instructor provides audio-visual instruction and physically interacts with human learners to effectively teach robotics and automation concepts and evaluate learner understanding of those concepts. In some examples, a training robot instructs and demonstrates encoder operation, feedback control, and robot motion coordination with external objects and events while physically interacting with a human learner. In some examples, interlock logic and waypoints of the training robot are programmed by the human user while the training robot physically interacts with the human learner. In a further aspect, a training robot evaluates the proficiency of a human learner with respect to particular robotic concepts. Future instruction by the training robot is determined in part by the measured proficiency.

The invention discloses a cloud expert system for improving efficiency of human learning research and decision making affair handling and belongs to the social application field. The system comprisesa human-computer interaction subsystem, an experience subsystem, a standard answer subsystem, a how-to-do subsystem, a product forum subsystem, a service forum subsystem, a technology forum subsystem,a topic matching interaction subsystem and various databases. Learning, research, decision making and affair handling are problems that human beings always face, repeated labor, fragmentation, wrongdecision making and affair mishandling are problems which led to a large amount of manpower, material and time losses in society, and development of the human society is delayed. The system is advantaged in that on the basis of analyzing impacts on human learning, research, decision making and affair handling efficiency, combined with the current scientific and technological conditions, the cloudexpert system is proposed and established, learning, research, decision making and affair handling are guided and assisted through following ideas and the knowledge of experts, and problems of errors,repetition and inefficiency in human learning research and decision making affair handling are solved.

The invention discloses a semantic matching method based on Bert, and the method is based on a pre-training model Bert-wwm-ext of Harbin Institute of Word, the model is firstly used to carry out unsupervised training of full word masks under our big data background, so that the model is firstly adapted to our data characteristics, and after the model based on our data is stored, the model based on our data is subjected to unsupervised training of full word masks under our big data background. The following adjustments are made on the structure of the model, a Pooling layer is added to an output layer of Bert, when sentences are input, each Batch inputs a group of specific sentences, a part of the sentences are similar in semantics, the remaining sentences are different in semantics, and in this way, the model is made to be similar to human learning, and the sentences can be input into the Bert. Contrast learning between data is considered, so that the model converges more quickly, after model architecture transformation is completed, sentence semantic similarity training is conducted again under the background of large corpora based on the model, comparison calculation between synonymous sentences and non-synonymous sentences is added in the training process, then the model is subjected to back propagation, and therefore the sentence semantic similarity is obtained. And finally obtained sentence vector semantic representation is more practical.

The invention discloses a sea water desalination system fault diagnosis method based on an improved selective evolution random network, and relates to the technical field of fault diagnosis, and the method comprises the following steps: selecting a plurality of classification data sets with large feature differences as an original network (PNN) to construct a data set; generating an initial random single hidden layer feedforward neural network; constructing a data set based on the PNN, and optimizing the network by adopting an adaptive human learning optimization algorithm (AHLOPID) based on intelligent PID control to obtain the PNN; enabling the PNN to be used for specific fault diagnosis, and carrying out actual working network optimization and feature selection through AHLOPID cooperation based on fault data of the sea water desalination system; and finally, enabling the obtained optimal classifier to be used for actual fault diagnosis. According to the method, the fault diagnosis generalization performance is improved by constructing the PNN, and the AHLOPID is used for network design to overcome instability caused by randomization in practical application of the random feedforward neural network, so that the fault diagnosis accuracy of the sea water desalination system is improved, and stable operation of the system is ensured.

The invention provides an aero-engine fault detection method based on IHPSO-KMSVDD. A multi-sphere support vector data description algorithm is proposed, sample data in different states are surrounded by different hyper-spheres for anomaly detection, the situation that a certain state is completely mistakenly recognized is greatly avoided, and the precision and robustness of an original algorithm are improved. Besides, aiming at the defect that the algorithm hyper-parameter training time is too long, the invention provides an improved particle swarm optimization algorithm for simulating human learning behaviors to optimize hyper-parameters, so that the training time can be effectively shortened. The algorithm is suitable for small and medium-scale classification problems, and has good performance in the aspect of aero-engine fault detection. When the aero-engine breaks down due to abrasion, corrosion, blockage and the like, health parameters of corresponding parts can be changed, and when normal data are doped with fault data of different degrees, the fault data can be continuously recognized with excellent performance under the condition that the fault data are mixed, and the working efficiency is effectively improved.

The invention relates to a system and a method for learning a gesture by a human learner (5), comprising the following steps: equipping said learner (5) with a plurality of motion sensors (6, 7) on a plurality of members that are predetermined in accordance with said gesture to be learned; acquiring biomechanical data provided by said plurality of sensors during a gesture performed by the learner; analyzing said acquired biomechanical data and determining a theoretical correction of the gesture by comparing said biomechanical data of the learner with biomechanical data corresponding to a target gesture; customizing the theoretical correction into a specific correction on the basis of behavior models of the learner derived from a history of biomechanical data acquired for the learner; transmitting said specific correction to the learner; updating said specific correction on the basis of information representing the sensation perceived by the learner when performing the corrected gesture.

The invention provides a neural network three-body model based on mesoscopic systems. The neural network three-body model comprises a learning body which is mainly used for construction from original data to a high-level mesoscopic system; a memory body which is mainly used for storing and recombining neuron attribute information in the different mesoscopic systems; and an interpretation body which is mainly used as a reverse propagation process of the learning body so as to realize a corresponding interpretation function of the high-level mesoscopic system to a low-level mesoscopic system. According to the invention, important nodes and intrinsic information in a deep learning process are stored by constructing the three-body neural network model based on the mesoscopic systems, and a memory organization structure is further formed, so that important basis and guarantee are provided for interpretability of deep learning, and a new direction is provided for the development of a brand new neural network which better conforms to a human learning cognition mode.