scholarly journals Real-time environmental analysis for industrial vehicles based on synthetic sensor data and deep learning

Procedia CIRP ◽  
2019 ◽  
Vol 81 ◽  
pp. 252-257
Author(s):  
Axel Börold ◽  
Michael Freitag
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Environmental Analysis ◽  
Sensor Data

scholarly journals A Study on the Real-time Fire Classification Algorithm Based on Stacked LSTM

2021 ◽  
Vol 21 (3) ◽  
pp. 93-104
Author(s):  
Yoseob Heo ◽  
Seongho Seo ◽  
We Shim ◽  
Jongseok Kang
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Dependence ◽  
Real Time ◽  
Sensor Data ◽  
Fire Monitoring ◽  
Monitoring Framework ◽  
Sensor Signals ◽  
Fire Detector ◽  
Using Data

Several researchers have been drawn to the development of fire detector in recent years, to protect people and property from the catastrophic disaster of fire. However, studies related to fire monitoring are affected by some unique characteristics of fire sensor signals, such as time dependence and the complexity of the signal pattern based on the variety of fire types,. In this study, a new deep learning-based approach that accurately classifies various types of fire situations in real-time using data obtained from multidimensional channel fire sensor signals was proposed. The contribution of this study is to develop a stacked-LSTM model that considers the time-series characteristics of sensor data and the complexity of multidimensional channel sensing data to develop a new fire monitoring framework for fire identification based on improving existing fire detectors.

scholarly journals Deep Learning Methods for monitoring, detecting and measuring Deer Movements for Wildlife Conservation

2019 ◽  
Vol 8 (4) ◽  
pp. 3303-3308
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Wildlife Conservation ◽  
State Of The Art ◽  
Sensor Data ◽  
Motion Sensor ◽  
Behavioral Studies ◽  
Object Proposals ◽  
Wildlife Populations ◽  
Free Ranging

Wildlife Researchers examine and dig video corpus for behavioral studies of free-ranging animals, which included monitoring, analyzing, classifying & detecting, managing, counting etc. Unfortunately, automated visual implementation for challenging real-time scenarios of wildlife is not an easy task especially for classification and recognition of wildlife-animals and estimate the sizes of wildlife populations. The aim of this paper is to bring state-of-the-art results from raw sensor data for learning features advancing automatic implementation and interpreting of animal movements from different perspectives. Also, turnout with an objectness score from object proposals generated by Region Proposal Network (RPN). The imagery data are captured from the motion sensor cameras and then through RCNN, Fast RCNN and Faster RCNN, it automatically are segmented and recognized the object with its objectness score. ConvNet automatically process these images and correctly recognizing the object. Experimentation results demonstrated prominent deer images with 96% accuracy with identifying three basic activities sleeping, grazing and resting. In addition, a measured implementation has been shown among CNN, RCNN, Fast RCNN and Faster RCNN.

Smart teaching mode based on particle swarm image recognition and human-computer interaction deep learning

2020 ◽  
Vol 39 (4) ◽  
pp. 5699-5711
Author(s):  
Shirong Long ◽  
Xuekong Zhao
Keyword(s):  
Feature Extraction ◽  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Real Time ◽  
Image Recognition ◽  
Particle Swarm ◽  
Learning Technology ◽  
Search Performance ◽  
Swarm Optimization ◽  
Teaching Mode

The smart teaching mode overcomes the shortcomings of traditional teaching online and offline, but there are certain deficiencies in the real-time feature extraction of teachers and students. In view of this, this study uses the particle swarm image recognition and deep learning technology to process the intelligent classroom video teaching image and extracts the classroom task features in real time and sends them to the teacher. In order to overcome the shortcomings of the premature convergence of the standard particle swarm optimization algorithm, an improved strategy for multiple particle swarm optimization algorithms is proposed. In order to improve the premature problem in the search performance algorithm of PSO algorithm, this paper combines the algorithm with the useful attributes of other algorithms to improve the particle diversity in the algorithm, enhance the global search ability of the particle, and achieve effective feature extraction. The research indicates that the method proposed in this paper has certain practical effects and can provide theoretical reference for subsequent related research.

Real-time Worker Safety Management System Using Deep Learning-based Video Analysis Algorithm

2020 ◽  
Vol 9 (3) ◽  
pp. 25-30
Author(s):  
So Yeon Jeon ◽  
Jong Hwa Park ◽  
Sang Byung Youn ◽  
Young Soo Kim ◽  
Yong Sung Lee ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Video Analysis ◽  
Management System ◽  
Safety Management ◽  
Worker Safety ◽  
Safety Management System ◽  
Analysis Algorithm

Real Time Gender and Age Prediction using Deep Learning Techniques

2020 ◽  
Vol 9 (6) ◽  
pp. 797-801
Keyword(s):  
Deep Learning ◽  
Face Recognition ◽  
Real Time ◽  
Vital Role ◽  
Learning Techniques ◽  
Gender And Age ◽  
The Face ◽  
And Gender ◽  
Gender Detection ◽  
Age Prediction

Face recognition plays a vital role in security purpose. In recent years, the researchers have focused on the pose illumination, face recognition, etc,. The traditional methods of face recognition focus on Open CV’s fisher faces which results in analyzing the face expressions and attributes. Deep learning method used in this proposed system is Convolutional Neural Network (CNN). Proposed work includes the following modules: [1] Face Detection [2] Gender Recognition [3] Age Prediction. Thus the results obtained from this work prove that real time age and gender detection using CNN provides better accuracy results compared to other existing approaches.

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