scholarly journals Student Behavior Recognition From Heterogeneous View Perception in Class Based on 3-D Multiscale Residual Dense Network for the Analysis of Case Teaching

2021 ◽  
Vol 15 ◽  
Author(s):  
Hui Liu ◽  
Yang Liu ◽  
Ran Zhang ◽  
Xia Wu
Keyword(s):  
Student Behavior ◽  
Recognition Algorithm ◽  
Point Of View ◽  
Learning Ability ◽  
Practical Significance ◽  
Dense Network ◽  
Behavior Recognition ◽  
Basic Module ◽  
Low Efficiency ◽  
Human Behavior Recognition

The study of student behavior analysis in class plays a key role in teaching and educational reforms that can help the university to find an effective way to improve students' learning efficiency and innovation ability. It is also one of the effective ways to cultivate innovative talents. The traditional behavior recognition methods have many disadvantages, such as poor robustness and low efficiency. From a heterogeneous view perception point of view, it introduces the students' behavior recognition. Therefore, we propose a 3-D multiscale residual dense network from heterogeneous view perception for analysis of student behavior recognition in class. First, the proposed method adopts 3-D multiscale residual dense blocks as the basic module of the network, and the module extracts the hierarchical features of students' behavior through the densely connected convolutional layer. Second, the local dense feature of student behavior is to learn adaptively. Third, the residual connection module is used to improve the training efficiency. Finally, experimental results show that the proposed algorithm has good robustness and transfer learning ability compared with the state-of-the-art behavior recognition algorithms, and it can effectively handle multiple video behavior recognition tasks. The design of an intelligent human behavior recognition algorithm has great practical significance to analyze the learning and teaching of students in the class.

scholarly journals Multifeature Fusion Human Motion Behavior Recognition Algorithm Using Deep Reinforcement Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Chengkun Lu
Keyword(s):  
Reinforcement Learning ◽  
Human Behavior ◽  
Performance Test ◽  
Recognition Algorithm ◽  
Human Motion ◽  
Identification Accuracy ◽  
Data Sets ◽  
Behavior Recognition ◽  
Data Set ◽  
Human Behavior Recognition

Through the recognition and analysis of human motion information, the actual motion state of human body can be obtained. However, the multifeature fusion of human behavior has limitations in recognition accuracy and robustness. Combined with deep reinforcement learning, multifeature fusion human behavior recognition is studied and we proposed a multifeature fusion human behavior recognition algorithm using deep reinforcement learning. Firstly, several typical human behavior data sets are selected as the research data in the benchmark data set. In the selected data sets, the behavior category contained in each video is the same behavior, and there are category tags. Secondly, the attention model is constructed. In the deep reinforcement learning network, the small sampling area is used as the model input. Finally, the corresponding position of the next visual area is estimated according to the time series information obtained after the input. The human behavior recognition algorithm based on deep reinforcement learning multifeature fusion is completed. The results show that the average accuracy of multifeature fusion of the algorithm is about 95%, the human behavior recognition effect is good, the identification accuracy rate is as high as about 98% and passed the camera movement impact performance test and the algorithm robustness, and the average time consumption of the algorithm is only 12.7 s, which shows that the algorithm has very broad application prospects.

Specimen Preparation for Clinical Applications of X-Ray Microanalysis

1990 ◽  
Vol 48 (2) ◽  
pp. 328-329
Author(s):  
J.D. Shelburne ◽  
G.M. Roomans
Keyword(s):  
Carbon Coating ◽  
Clinical Applications ◽  
Point Of View ◽  
Practical Significance ◽  
Sweat Test ◽  
Specimen Preparation ◽  
Tissue Preparation ◽  
X Ray ◽  
Gallbladder Stones ◽  
Clinical Problems

Proper preparative procedures are a prerequisite for the validity of the results of x-ray microanalysis of biological tissue. Clinical applications of x-ray microanalysis are often concerned with diagnostic problems and the results may have profound practical significance for the patient. From this point of view it is especially important that specimen preparation for clinical applications is carried out correctly.Some clinical problems require very little tissue preparation. Hair, nails, and kidney and gallbladder stones may be examined and analyzed after carbon coating. High levels of zinc or copper in hair may be indicative of dermatological or systemic diseases. Nail clippings may be analyzed (as an alternative to the more conventional sweat test) to confirm a diagnosis of cystic fibrosis. X-ray microanalysis in combination with scanning electron microscopy has been shown to be the most reliable method for the identification of the components of kidney or gallbladder stones.A quantitatively very important clinical application of x-ray microanalysis is the identification and quantification of asbestos and other exogenous particles in lung.

scholarly journals Speech recognition algorithm for natural language management systems under variety of accents

2020 ◽  
Vol 164 ◽  
pp. 10015
Author(s):  
Irina Gurtueva ◽  
Olga Nagoeva ◽  
Inna Pshenokova
Keyword(s):  
Speech Recognition ◽  
Recognition Algorithm ◽  
Individual Characteristics ◽  
Point Of View ◽  
Language Management ◽  
Universal System ◽  
Sound Image ◽  
Behavioral Studies ◽  
Multi Agent ◽  
The Individual

This paper proposes a concept of a new approach to the development of speech recognition systems using multi-agent neurocognitive modeling. The fundamental foundations of these developments are based on the theory of cognitive psychology and neuroscience, and advances in computer science. The purpose of this work is the development of general theoretical principles of sound image recognition by an intelligent robot and, as the sequence, the development of a universal system of automatic speech recognition, resistant to speech variability, not only with respect to the individual characteristics of the speaker, but also with respect to the diversity of accents. Based on the analysis of experimental data obtained from behavioral studies, as well as theoretical model ideas about the mechanisms of speech recognition from the point of view of psycholinguistic knowledge, an algorithm resistant to variety of accents for machine learning with imitation of the formation of a person’s phonemic hearing has been developed.

3D Human behavior recognition based on binocular vision and face–hand feature

Optik ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. 4712-4717 ◽  
Author(s):  
Qing Ye ◽  
Junfeng Dong ◽  
Yongmei Zhang
Keyword(s):  
Human Behavior ◽  
Binocular Vision ◽  
Behavior Recognition ◽  
Human Behavior Recognition

Based on spatio-temporal graph convolution networks with residual connection for intelligence behavior recognition

2021 ◽  
pp. 002072092199660
Author(s):  
Yinong Zhang ◽  
Shanshan Guan ◽  
Cheng Xu ◽  
Hongzhe Liu
Keyword(s):  
Human Behavior ◽  
Behavior Recognition ◽  
Convolutional Network ◽  
Skeleton Model ◽  
Basic Technology ◽  
Temporal Model ◽  
Temporal Graph ◽  
Spatio Temporal ◽  
Human Behavior Recognition ◽  
Behavior Characteristics

In the era of intelligent education, human behavior recognition based on computer vision is an important branch of pattern recognition. Human behavior recognition is a basic technology in the fields of intelligent monitoring and human-computer interaction in education. The dynamic changes of human skeleton provide important information for the recognition of educational behavior. Traditional methods usually use manual information to label or traverse rules only, resulting in limited representation capabilities and poor generalization performance of the model. In this paper, a kind of dynamic skeleton model with residual is adopted—a spatio-temporal graph convolutional network based on residual connections, which not only overcomes the limitations of previous methods, but also can learn the spatio-temporal model from the skeleton data. In the big bone NTU-RGB + D dataset, the network model not only improved the representation ability of human behavior characteristics, but also improved the generalization ability, and achieved better recognition effect than the existing model. In addition, this paper also compares the results of behavior recognition on subsets of different joint points, and finds that spatial structure division have better effects.

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