Machine Learning Based Grain Moisture Estimation for Real-time Monitoring of High-Temperature Paddy Drying Silo

2020 ◽  
Author(s):  
Farooq Ahmad ◽  
M Shahzad Younis ◽  
Rana Usman Zahid ◽  
Liaqat Ali Shahid
Keyword(s):  
Machine Learning ◽  
High Temperature ◽  
Real Time ◽  
Real Time Monitoring ◽  
Grain Moisture ◽  
Moisture Estimation

REAL-TIME MONITORING OF GRAIN MOISTURE IN THE FIELD

2021 ◽  
Author(s):  
P Kalandarov ◽  
G Aralov ◽  
O Norboyev
Keyword(s):  
Real Time ◽  
Real Time Monitoring ◽  
Grain Moisture ◽  
Real Time Information ◽  
Time Information

The article deals with the method of measuring grain moisture when it is collected in the field, analyzes the synthesis of a real-time information andmeasurement system.

scholarly journals Waste Management and Prediction of Air Pollutants Using IoT and Machine Learning Approach

Energies ◽  
2020 ◽  
Vol 13 (15) ◽  
pp. 3930 ◽  
Author(s):  
Ayaz Hussain ◽  
Umar Draz ◽  
Tariq Ali ◽  
Saman Tariq ◽  
Muhammad Irfan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Real Time ◽  
Air Pollutants ◽  
Garbage Collection ◽  
Air Pollutant ◽  
Real Time Monitoring ◽  
Collection System ◽  
The Real ◽  
The Status

Increasing waste generation has become a significant issue over the globe due to the rapid increase in urbanization and industrialization. In the literature, many issues that have a direct impact on the increase of waste and the improper disposal of waste have been investigated. Most of the existing work in the literature has focused on providing a cost-efficient solution for the monitoring of garbage collection system using the Internet of Things (IoT). Though an IoT-based solution provides the real-time monitoring of a garbage collection system, it is limited to control the spreading of overspill and bad odor blowout gasses. The poor and inadequate disposal of waste produces toxic gases, and radiation in the environment has adverse effects on human health, the greenhouse system, and global warming. While considering the importance of air pollutants, it is imperative to monitor and forecast the concentration of air pollutants in addition to the management of the waste. In this paper, we present and IoT-based smart bin using a machine and deep learning model to manage the disposal of garbage and to forecast the air pollutant present in the surrounding bin environment. The smart bin is connected to an IoT-based server, the Google Cloud Server (GCP), which performs the computation necessary for predicting the status of the bin and for forecasting air quality based on real-time data. We experimented with a traditional model (k-nearest neighbors algorithm (k-NN) and logistic reg) and a non-traditional (long short term memory (LSTM) network-based deep learning) algorithm for the creation of alert messages regarding bin status and forecasting the amount of air pollutant carbon monoxide (CO) present in the air at a specific instance. The recalls of logistic regression and k-NN algorithm is 79% and 83%, respectively, in a real-time testing environment for predicting the status of the bin. The accuracy of modified LSTM and simple LSTM models is 90% and 88%, respectively, to predict the future concentration of gases present in the air. The system resulted in a delay of 4 s in the creation and transmission of the alert message to a sanitary worker. The system provided the real-time monitoring of garbage levels along with notifications from the alert mechanism. The proposed works provide improved accuracy by utilizing machine learning as compared to existing solutions based on simple approaches.

Telescope performance real-time monitoring based on machine learning

2020 ◽  
Vol 500 (1) ◽  
pp. 388-396
Author(s):  
Tian Z Hu ◽  
Yong Zhang ◽  
Xiang Q Cui ◽  
Qing Y Zhang ◽  
Ye P Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
System Performance ◽  
High Performance ◽  
High Efficiency ◽  
Peak Performance ◽  
Source Image ◽  
Detection Accuracy ◽  
Real Time Monitoring ◽  
Active Optics

ABSTRACT In astronomy, the demand for high-resolution imaging and high-efficiency observation requires telescopes that are maintained at peak performance. To improve telescope performance, it is useful to conduct real-time monitoring of the telescope status and detailed recordings of the operational data of the telescope. In this paper, we provide a method based on machine learning to monitor the telescope performance in real-time. First, we use picture features and the random forest algorithm to select normal pictures captured by the acquisition camera or science camera. Next, we cut out the source image of the picture and use convolutional neural networks to recognize star shapes. Finally, we monitor the telescope performance based on the relationship between the source image shape and telescope performance. Through this method, we achieve high-performance real-time monitoring with the Large Sky Area Multi-Object Fibre Spectroscopic Telescope, including guiding system performance, focal surface defocus, submirror performance, and active optics system performance. The ultimate performance detection accuracy can reach up to 96.7 per cent.

scholarly journals Optical system for real-time monitoring of nuclear fuel pellets at high temperature

2020 ◽  
Vol 357 ◽  
pp. 110383
Author(s):  
Thibault Vidal ◽  
Laurent Gallais ◽  
Romain Burla ◽  
Frederic Martin ◽  
Hélène Capdevila ◽  
...  
Keyword(s):  
High Temperature ◽  
Real Time ◽  
Nuclear Fuel ◽  
Optical System ◽  
Real Time Monitoring ◽  
Fuel Pellets ◽  
Nuclear Fuel Pellets

scholarly journals The Estimation of Chemical Oxygen Demand of Erhai Lake Basin and Its Links with DOM Fluorescent Components Using Machine Learning

Water ◽  
2021 ◽  
Vol 13 (24) ◽  
pp. 3629
Author(s):  
Yuquan Zhao ◽  
Jian Shen ◽  
Jimeng Feng ◽  
Zhitong Sun ◽  
Tianyang Sun ◽  
...  
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
Chemical Oxygen Demand ◽  
Real Time ◽  
Oxygen Demand ◽  
Organic Pollution ◽  
Lake Basin ◽  
Erhai Lake ◽  
Real Time Monitoring ◽  
The Relationship

Water quality estimation tools based on real-time monitoring are essential for the effective management of organic pollution in watersheds. This study aims to monitor changes in the levels of chemical oxygen demand (COD, CODMn) and dissolved organic matter (DOM) in Erhai Lake Basin, exploring their relationships and the ability of DOM to estimate COD and CODMn. Excitation emission matrix–parallel factor analysis (EEM–PARAFAC) of DOM identified protein-like component (C1) and humic-like components (C2, C3, C4). Combined with random forest (RF), maximum fluorescence intensity (Fmax) values of components were selected as estimation parameters to establish models. Results proved that the COD of rivers was more sensitive to the reduction in C1 and C2, while CODMn was more sensitive to C4. The DOM of Erhai Lake thrived by internal sources, and the relationship between COD, CODMn, and DOM of Erhai Lake was more complicated than rivers (inflow rivers of Erhai Lake). Models for rivers achieved good estimations, and by adding dissolved oxygen and water temperature, the estimation ability of COD models for Erhai Lake was significantly improved. This study demonstrates that DOM-based machine learning can be used as an alternative tool for real-time monitoring of organic pollution and deepening the understanding of the relationship between COD, CODMn, and DOM, and provide a scientific basis for water quality management.

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