CNN models for eye state classification using EEG with temporal ordering

2021 ◽  
Author(s):  
Femi William ◽  
Feng Zhu
Keyword(s):  
State Classification ◽  
Temporal Ordering

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.

Two Candidate Approaches o Text Sequencing: An Addendum to “The Diachrony Debate: A Tutorial on Methods”

2018 ◽  
Vol 26 (2) ◽  
Author(s):  
Dean A. Forbes
Keyword(s):  
Computational Biology ◽  
Middle English ◽  
Diagnostic Tool ◽  
Reliable Method ◽  
Biblical Hebrew ◽  
Quantitative Comparison ◽  
Curve Analysis ◽  
Single Feature ◽  
S Curve ◽  
Temporal Ordering

In a recent essay published in this journal, I illustrated the limitations one may encounter when sequencing texts temporally using s-curve analysis. I also introduced seriation, a more reliable method for temporal ordering much used in both archaeology and computational biology. Lacking independently ordered Biblical Hebrew (BH) data to assess the potential power of seriation in the context of diachronic studies, I used classic Middle English data originally compiled by Ellegård. In this addendum, I reintroduce and extend s-curve analysis, applying it to one rather noisy feature of Middle English. My results support Holmstedt’s assertion that s-curve analysis can be a useful diagnostic tool in diachronic studies. Upon quantitative comparison, however, the five-feature seriation results derived in my former paper are found to be seven times more accurate than the single-feature s-curve results presented here. 

The Effects of Day-to-Day Variability of Physiological Data on Operator Functional State Classification

2013 ◽  
Author(s):  
James C. Christensen ◽  
Justin R. Estepp ◽  
Glenn F. Wilson ◽  
Christopher A. Russell ◽  
Krystal M. Thomas
Keyword(s):  
Functional State ◽  
Physiological Data ◽  
State Classification ◽  
Operator Functional State

scholarly journals Real-time 2D–3D door detection and state classification on a low-power device

2021 ◽  
Vol 3 (5) ◽  
Author(s):  
João Gaspar Ramôa ◽  
Vasco Lopes ◽  
Luís A. Alexandre ◽  
S. Mogo
Keyword(s):  
Low Power ◽  
Real Time ◽  
Object Classification ◽  
Semantic Segmentation ◽  
Detection Algorithm ◽  
Power Device ◽  
Indoor Environments ◽  
State Classification ◽  
Segmentation Algorithms ◽  
Indoor Spaces

AbstractIn this paper, we propose three methods for door state classification with the goal to improve robot navigation in indoor spaces. These methods were also developed to be used in other areas and applications since they are not limited to door detection as other related works are. Our methods work offline, in low-powered computers as the Jetson Nano, in real-time with the ability to differentiate between open, closed and semi-open doors. We use the 3D object classification, PointNet, real-time semantic segmentation algorithms such as, FastFCN, FC-HarDNet, SegNet and BiSeNet, the object detection algorithm, DetectNet and 2D object classification networks, AlexNet and GoogleNet. We built a 3D and RGB door dataset with images from several indoor environments using a 3D Realsense camera D435. This dataset is freely available online. All methods are analysed taking into account their accuracy and the speed of the algorithm in a low powered computer. We conclude that it is possible to have a door classification algorithm running in real-time on a low-power device.

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