scholarly journals Recognition of Psychological Characteristics of Students’ Behavior Based on Improved Machine Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Mingchao Li
Keyword(s):  
Classroom Behavior ◽  
Feature Recognition ◽  
Recognition Algorithm ◽  
Psychological Characteristics ◽  
Classroom Teaching ◽  
Analysis Model ◽  
Characteristic Extraction ◽  
Psychological Feature ◽  
Extraction Algorithm ◽  
Systemic Model

Contemporary classroom teaching requires the combination of students’ classroom behavior and their psychological activities and appropriately changes the teaching mode according to students’ psychological characteristics. This paper analyzes the traditional characteristic recognition algorithm, and after improving its deficiencies, an improved characteristic extraction algorithm is proposed, based on the actual situation of classroom learning. This new algorithm can effectively improve the students’ psychological feature prediction; with the support of this algorithm, a comprehensive analysis model with classroom behavior recognition and psychological feature recognition is constructed; also, the functional structure of the system is built up. Through experimental research, the model proposed in this paper is analyzed, and the experimental data has approved that the systemic model could play an important role in classroom teaching.

Automatic Control System Fault Image Recognition Technology

2014 ◽  
Vol 513-517 ◽  
pp. 4111-4114
Author(s):  
Zhong Guo Yang ◽  
Tian Fang Cai
Keyword(s):  
Control System ◽  
Automatic Control ◽  
Image Recognition ◽  
Automatic Control System ◽  
Recognition Algorithm ◽  
Module Design ◽  
Characteristic Extraction ◽  
Extraction Algorithm ◽  
And Control ◽  
Fault Characteristic

The inspection methods and ways of automatic control system fault positions directly link to the feasibility of products. The paper bases on basis principles of image recognition, studies on the module design of automatic control system images and fault image recognition algorithm, puts forward depressed windowing grey level linear partitioning characteristic extraction algorithm and control system fault characteristic recognition algorithm and check its feasibility.

Recognition of classroom student state features based on deep learning algorithms and machine learning

2020 ◽  
pp. 1-12
Author(s):  
Hu Jingchao ◽  
Haiying Zhang
Keyword(s):  
Deep Learning ◽  
Feature Recognition ◽  
Subjective Evaluation ◽  
Recognition Algorithm ◽  
Video Data ◽  
State Recognition ◽  
Detection Model ◽  
State Classification ◽  
Intelligent Models ◽  
Model Recognition

The difficulty in class student state recognition is how to make feature judgments based on student facial expressions and movement state. At present, some intelligent models are not accurate in class student state recognition. In order to improve the model recognition effect, this study builds a two-level state detection framework based on deep learning and HMM feature recognition algorithm, and expands it as a multi-level detection model through a reasonable state classification method. In addition, this study selects continuous HMM or deep learning to reflect the dynamic generation characteristics of fatigue, and designs random human fatigue recognition experiments to complete the collection and preprocessing of EEG data, facial video data, and subjective evaluation data of classroom students. In addition to this, this study discretizes the feature indicators and builds a student state recognition model. Finally, the performance of the algorithm proposed in this paper is analyzed through experiments. The research results show that the algorithm proposed in this paper has certain advantages over the traditional algorithm in the recognition of classroom student state features.

Simulation of classroom student behavior recognition based on PSO-kNN algorithm and emotional image processing

2020 ◽  
pp. 1-11
Author(s):  
Shilong Wu
Keyword(s):  
Image Processing ◽  
Emotion Recognition ◽  
Student Behavior ◽  
Classroom Behavior ◽  
Feature Recognition ◽  
Pso Algorithm ◽  
Cluster Center ◽  
Image Processing Algorithm ◽  
Behavior Recognition ◽  
Key Frame

Students’ classroom behavior recognition and emotion recognition effects directly determine the degree of teachers’ control of the classroom teaching process. At present, teachers and students belong to two groups in traditional teaching, and teachers cannot effectively mobilize students’ learning emotions. In order to improve the teaching effect, this paper combines the PSO algorithm and the KNN algorithm to obtain the PSO-KNN joint algorithm, and combines with the emotional image processing algorithm to construct an artificial intelligence-based classroom student behavior recognition model. Moreover, based on the image processing technology, this paper uses key frame detection for feature recognition, and this paper improves the recognition process based on the inter-frame similarity measurement algorithm and initial cluster center selection in the key frame extraction method of clustering. In addition, this paper analyzes the effect of the model constructed on the behavior recognition and emotion recognition of students. The research results show that the joint algorithm constructed in this paper has a high accuracy rate for students’ emotion recognition and behavior recognition, and can meet the actual teaching needs.

Midsurface Abstraction From 3D Solid Models for Engineering Analysis

1996 ◽  
Author(s):  
Mohsen Rezayat
Keyword(s):  
Feature Recognition ◽  
Analysis Model ◽  
Thin Sections ◽  
New Approach ◽  
Solid Models ◽  
Product And Process ◽  
Plastic Parts ◽  
3D Solid ◽  
User Friendly ◽  
Analysis Models

Abstract An integral part of implementing parallel product and process designs is simulation through numerical analysis. This simulation-driven design requires discretization of the 3D part in an appropriate manner. If the part is thin or has thin sections (e.g., plastic parts), then an analysis model with reduced dimensionality may be more accurate and economical than a standard 3D model. In addition, substantial simplification of some details in the design geometry may be beneficial and desirable in the analysis model. Unfortunately, the majority of CAD systems do not provide the means for abstraction of appropriate analysis models. In this paper we present a new approach, based on midsurface abstraction, which holds significant promise in simplifying simulation-driven design. The method is user-friendly because very little interaction is required to guide the software in its automatic creation of the desired analysis model. It is also robust because it handles typical parts with complex and interacting features. Application of the method for feature recognition and abstraction is also briefly discussed.

scholarly journals Convolutional Neural Network-Assisted Strategies for Improving Teaching Quality of College English Flipped Class

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Tiankun Liu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Student Behavior ◽  
Flipped Classroom ◽  
Classroom Behavior ◽  
Procedural Knowledge ◽  
Teaching Style ◽  
Teaching Quality ◽  
Classroom Teaching ◽  
College English

The “flipped classroom” teaching paradigm not only follows the cognitive rules of the learners, but it also subverts and reverses the standard classroom teaching process. Problem-oriented, teacher-led, student-centered, and mixed teaching approaches are the key teaching methods in the flipped classroom teaching model, which focuses on students’ procedural knowledge acquisition and critical thinking training. There are a lot of studies on the specific practice path of the “flipped classroom” teaching style right now, but there are not many on the learning involvement of college English students in this approach. According to studies, the level of student participation in classroom learning is the most important factor limiting the efficiency of teaching. The lack of research in this subject greatly limits the “flipped classroom” teaching model’s ability to improve college English classroom teaching quality. The degree of engagement between teachers and students, the enthusiasm of students in class, and the competence of teachers to educate are all reflected in student conduct in the classroom. Understanding and evaluating the behaviors and activities of students in the classroom are helpful in determining the state of students in the classroom, as well as improving the flipped classroom teaching technique and quality. As a result, the convolutional neural network is used to recognize student behavior in the classroom. The loss function of VGG-16 has been enhanced, the distance inside the class has been lowered, the distance between classes has been increased, and the recognition accuracy has improved. Accurate recognition of classroom behavior is beneficial in developing methods to improve teaching quality.

scholarly journals Key Feature Recognition Algorithm of Network Intrusion Signal Based on Neural Network and Support Vector Machine

Symmetry ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 380 ◽  
Author(s):  
Kai Ye
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Feature Recognition ◽  
False Positive Rate ◽  
Principal Component ◽  
Recognition Algorithm ◽  
Support Vector ◽  
Data Set ◽  
Network Intrusion ◽  
Key Features

When identifying the key features of the network intrusion signal based on the GA-RBF algorithm (using the genetic algorithm to optimize the radial basis) to identify the key features of the network intrusion signal, the pre-processing process of the network intrusion signal data is neglected, resulting in an increase in network signal data noise, reducing the accuracy of key feature recognition. Therefore, a key feature recognition algorithm for network intrusion signals based on neural network and support vector machine is proposed. The principal component neural network (PCNN) is used to extract the characteristics of the network intrusion signal and the support vector machine multi-classifier is constructed. The feature extraction result is input into the support vector machine classifier. Combined with PCNN and SVM (Support Vector Machine) algorithms, the key features of network intrusion signals are identified. The experimental results show that the algorithm has the advantages of high precision, low false positive rate and the recognition time of key features of R2L (it is a common way of network intrusion attack) data set is only 3.18 ms.

Automatic Recognition of Aircraft Noise with PLP Method

2012 ◽  
Vol 160 ◽  
pp. 145-149 ◽  
Author(s):  
Jian Li Ding ◽  
Yong Yang
Keyword(s):  
Feature Extraction ◽  
Filter Bank ◽  
Recognition Algorithm ◽  
Automatic Recognition ◽  
Aircraft Noise ◽  
Predictive Analysis ◽  
Modified Method ◽  
Extraction Algorithm ◽  
Auditory Feature ◽  
Linear Predictive Analysis

This paper proposes a modified auditory feature extraction algorithm based on perceptual linear predictive analysis which is more suitable for automatic recognition of aircraft noise. In this algorithm, a different distribution of filter-bank is introduced in order to fit the physical characteristic of aircraft noise and the result shows that the modified method indeed performs better. The effect of Gammatone filter in improving the robustness of recognition algorithm is also demonstrated in the experiment.

RECOGNITION OF NUMERAL STRINGS ON MAPS

1999 ◽  
Vol 13 (03) ◽  
pp. 319-337 ◽  
Author(s):  
LIANG-HUA CHEN ◽  
JIING-YUH WANG
Keyword(s):  
Information Systems ◽  
Geographic Information Systems ◽  
Recognition Algorithm ◽  
Data Capture ◽  
Automatic Data ◽  
Cadastral Maps ◽  
Extraction Algorithm ◽  
Complete Procedure ◽  
Feature Based ◽  
Position And Orientation

This paper presents a complete procedure for the extraction and recognition of hand-printed numeral strings on maps. The extraction algorithm can extract individual characters from a map even if the characters touch each other or touch with graphical line. The feature-based recognition algorithm can recognize numeral characters of any size, position and orientation. Our features for discrimination are simple and easily detectable. Experimental results on utility and cadastral maps have shown that the proposed technique is effective in the automatic data capture of geographic information systems.

High Resolution RS Image Industrial Solid Wastes Extraction Based on SVM

2014 ◽  
Vol 543-547 ◽  
pp. 2318-2322
Author(s):  
Xiu Hong Yao ◽  
Wen Xing Bao
Keyword(s):  
High Resolution ◽  
Anisotropic Diffusion ◽  
Feature Recognition ◽  
Solid Wastes ◽  
Shape Features ◽  
Operation Efficiency ◽  
Diffusion Filtering ◽  
Extraction Algorithm ◽  
Image Pretreatment ◽  
Fast Extraction

In order to accurately extract various types of industrial solid wastes from high resolution RS images, a industrial solid wastes feature fast extraction algorithm was proposed based on SVM. The reasonable image pretreatment was conducted by anisotropic diffusion filtering firstly. It is because that high resolution RS image contains abundant information and industrial solid wastes heap was very complex, we proposed the classification algorithm based on 1-v-1 which could extract multi-class industrial solid wastes fast and accurately at once. The new algorithm improved both efficiency and accuracy of industrial solid wastes recognition. The experimental results show that the industrial solid wastes feature recognition of SVM has better advantages than conventional methods. The new algorithm can recognize not only shape features of industrial solid wastes heap but also its material and type and it is constructed to recognize multi-class industrial solid wastes with higher operation efficiency.

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