scholarly journals A Study on Meteor Head Echo Using a Probabilistic Detection Model at Jicamarca

2020 ◽  
Vol 125 (1) ◽  
Author(s):  
Yanlin Li ◽  
Qihou Zhou ◽  
Mark Scott ◽  
Marco Milla
Keyword(s):  
Detection Model ◽  
Probabilistic Detection ◽  
Head Echo

scholarly journals Distributed Multitarget Probabilistic Coverage Control Algorithm for Wireless Sensor Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Ying Tian ◽  
Yang Ou ◽  
Hamid Reza Karimi ◽  
Yan Tao Liu ◽  
Jian Qun Han
Keyword(s):  
Control Algorithm ◽  
Network Size ◽  
Wireless Sensor ◽  
Coverage Problem ◽  
Active Sensors ◽  
Detection Model ◽  
Coverage Control ◽  
Energy Limitation ◽  
Probabilistic Detection ◽  
Probabilistic Coverage

This paper is concerned with the problem of multitarget coverage based on probabilistic detection model. Coverage configuration is an effective method to alleviate the energy-limitation problem of sensors. Firstly, considering the attenuation of node’s sensing ability, the target probabilistic coverage problem is defined and formalized, which is based on Neyman-Peason probabilistic detection model. Secondly, in order to turn off redundant sensors, a simplified judging rule is derived, which makes the probabilistic coverage judgment execute on each node locally. Thirdly, a distributed node schedule scheme is proposed for implementing the distributed algorithm. Simulation results show that this algorithm is robust to the change of network size, and when compared with the physical coverage algorithm, it can effectively minimize the number of active sensors, which guarantees all the targetsγ-covered.

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.

Detection model of similar datasets

2019 ◽  
Author(s):  
Julio Acedo ◽  
Marcos Fernandez-Sellers ◽  
Adolfo Lozano-Tello
Keyword(s):  
Detection Model
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