scholarly journals Electrocardiogram Feature Recognition Algorithm with Windowing and Adaptive Thresholding

2019 ◽  
Vol 1201 ◽  
pp. 012048
Author(s):  
S I Purnama ◽  
H Kusuma ◽  
T A Sardjono
Keyword(s):  
Feature Recognition ◽  
Recognition Algorithm ◽  
Adaptive Thresholding

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.

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.

A feature complexity index

1998 ◽  
Vol 212 (5) ◽  
pp. 405-412 ◽  
Author(s):  
G Little ◽  
R Tuttle ◽  
D E R Clark ◽  
J Corney
Keyword(s):  
Feature Recognition ◽  
Three Dimensional ◽  
Recognition Algorithm ◽  
Solid Model ◽  
Geometric Complexity ◽  
Relative Complexity ◽  
Complexity Index

An index is presented for quantifying the geometric complexity of a three-dimensional solid model. This provides a measure by which components may be compared one with another in relation to their relative complexity. The index is alphanumeric and readily computable. Such an index can be of use in the field of feature recognition as a means to determine how efficiently one algorithm handles components of varying complexity compared to another such algorithm. The performance of the authors’ own feature recognition algorithm is tested against components of differing complexity as determined by the index.

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.

Vehicle license plate recognition method based on deep convolution network in complex road scene

2019 ◽  
Vol 233 (9) ◽  
pp. 2284-2292
Author(s):  
Ze Liu ◽  
Yingfeng Cai ◽  
Long Chen ◽  
Hai Wang ◽  
Youguo He
Keyword(s):  
Feature Recognition ◽  
Recognition Algorithm ◽  
License Plate ◽  
License Plate Recognition ◽  
Depth Analysis ◽  
Robust Recognition ◽  
Character Feature ◽  
Deep Convolution Network ◽  
Nonlinear Operation ◽  
Better Than

The license plate robust recognition algorithm in complex road scene has both theoretical and practical values. The existing license plate recognition algorithm can achieve better recognition results under ideal road scenes such as moderate light intensity, good shooting angle, and clear license plate target, but in complex road scenes such as fast speed, blurred aging of license plates, and low illumination such as rainy days, the effectiveness of the license plate recognition algorithm still needs to be improved. Based on the realistic requirements of license plate recognition algorithm and in-depth analysis of the principle of deep convolution network, we designed a deep convolution network for Chinese characters, letters, and numbers in the license plate to automatically learn the essential features of the image to make up for the limitation of the artificial feature recognition of the traditional license plate recognition algorithm. At the same time, according to the convolution kernel, downsampling, and nonlinear operation of the deep convolution network, the nonlinear abstraction ability of the license plate character feature is improved. The experimental results show that the proposed method can quickly and accurately identify the license plate character in complex road scenes. The recognition accuracy is better than the existing license plate recognition algorithm, which improves the accuracy of license plate recognition and achieves an ideal license plate recognition effect.

scholarly journals A Pointer Type Instrument Intelligent Reading System Design Based on Convolutional Neural Networks

2020 ◽  
Vol 8 ◽  
Author(s):  
Yue Lin ◽  
Qinghua Zhong ◽  
Hailing Sun
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Feature Recognition ◽  
Recognition Rate ◽  
Recognition Algorithm ◽  
Operating Conditions ◽  
Automatic Identification ◽  
Online Reading ◽  
Reading System

The pointer instrument has the advantages of being simple, reliable, stable, easy to maintain, having strong anti-interference properties, and so on, which has long occupied the main position of electrical and electric instruments. Though the pointer instrument structure is simple, it is not convenient for real-time reading of measurements. In this paper, a RK3399 microcomputer was used for real-time intelligent reading of a pointer instrument using a camera. Firstly, a histogram normalization transform algorithm was used to optimize the brightness and enhance the contrast of images; then, the feature recognition algorithm You Only Look Once 3rd (YOLOv3) was used to detect and capture the panel area in images; and Convolutional Neural Networks were used to read and predict the characteristic images. Finally, predicted results were uploaded to a server. The system realized automatic identification, numerical reading, an intelligent online reading of pointer data, which has high feasibility and practical value. The experimental results show that the recognition rate of this system was 98.71% and the reading accuracy was 97.42%. What is more, the system can accurately locate the pointer-type instrument area and read corresponding values with simple operating conditions. This achievement meets the demand of real-time readings for analog instruments.

Feature Recognition for Virtual Environments

2014 ◽  
Vol 610 ◽  
pp. 642-646
Author(s):  
Mong Heng Ear ◽  
Cheng Cheng ◽  
Salem Mostafa Hamdy ◽  
Alhazmi Marwah
Keyword(s):  
Virtual Environments ◽  
Data Exchange ◽  
Feature Recognition ◽  
Virtual Assembly ◽  
Recognition Algorithm ◽  
Boundary Representation ◽  
Adjacency Graph ◽  
Levels Of Detail ◽  
Data Source ◽  
Design Systems

This paper demonstrates methods to recognize 3D designed features for virtual environments and apply them to Virtual assembly. STEP is a standard of Product data Exchange for interfacing different design systems, but it cannot be used as input for virtual environments. In order to use feature data in virtual assembly environments, main data source from a STEP file should be recognized and features should be re-built. First, Attributed Adjacency Graph (AAG) is used to analyze and express the boundary representation; second, a feature-tree of a part is constructed; third, using the AAG and feature-tree as inputs, we analyze and extract of features with a feature recognition algorithm; finally, various levels of detail of object geometric shapes is built and expressed in XML for virtual assembly applications.

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