A method for predicting hobbing tool wear based on CNC real-time monitoring data and deep learning

2021 ◽  
Author(s):  
Dashuang Wang ◽  
Rongjing Hong ◽  
Xiaochuan Lin
Keyword(s):  
Deep Learning ◽  
Tool Wear ◽  
Real Time ◽  
Monitoring Data ◽  
Real Time Monitoring

scholarly journals Comparison of Forecasting Models for Real-Time Monitoring of Water Quality Parameters Based on Hybrid Deep Learning Neural Networks

Water ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 1547
Author(s):  
Jian Sha ◽  
Xue Li ◽  
Man Zhang ◽  
Zhong-Liang Wang
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Deep Learning ◽  
Real Time ◽  
Best Management Practices ◽  
Input Data ◽  
Management Practices ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Real Time Monitoring

Accurate real-time water quality prediction is of great significance for local environmental managers to deal with upcoming events and emergencies to develop best management practices. In this study, the performances in real-time water quality forecasting based on different deep learning (DL) models with different input data pre-processing methods were compared. There were three popular DL models concerned, including the convolutional neural network (CNN), long short-term memory neural network (LSTM), and hybrid CNN–LSTM. Two types of input data were applied, including the original one-dimensional time series and the two-dimensional grey image based on the complete ensemble empirical mode decomposition algorithm with adaptive noise (CEEMDAN) decomposition. Each type of input data was used in each DL model to forecast the real-time monitoring water quality parameters of dissolved oxygen (DO) and total nitrogen (TN). The results showed that (1) the performances of CNN–LSTM were superior to the standalone model CNN and LSTM; (2) the models used CEEMDAN-based input data performed much better than the models used the original input data, while the improvements for non-periodic parameter TN were much greater than that for periodic parameter DO; and (3) the model accuracies gradually decreased with the increase of prediction steps, while the original input data decayed faster than the CEEMDAN-based input data and the non-periodic parameter TN decayed faster than the periodic parameter DO. Overall, the input data preprocessed by the CEEMDAN method could effectively improve the forecasting performances of deep learning models, and this improvement was especially significant for non-periodic parameters of TN.

Sources of Variability in Real-Time Monitoring Data for Fine Particulate Matter: Comparability of Three Wearable Monitors in an Urban Setting

2019 ◽  
Vol 6 (4) ◽  
pp. 222-227 ◽  
Author(s):  
Jared A. Fisher ◽  
Melissa C. Friesen ◽  
Sungduk Kim ◽  
Sarah J. Locke ◽  
Yonathan Kefelegn ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Real Time ◽  
Fine Particulate Matter ◽  
Urban Setting ◽  
Monitoring Data ◽  
Real Time Monitoring ◽  
Fine Particulate

Moving Boundary Simulation of Iron-Zinc Sacrificial Corrosion under Dynamic Electrolyte Thickness Based on Real-Time Monitoring Data

2020 ◽  
Vol 167 (4) ◽  
pp. 041503 ◽  
Author(s):  
Mohsen Saeedikhani ◽  
Nils Van den Steen ◽  
Sudesh Wijesinghe ◽  
Sareh Vafakhah ◽  
Herman Terryn ◽  
...  
Keyword(s):  
Real Time ◽  
Moving Boundary ◽  
Monitoring Data ◽  
Real Time Monitoring

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.

scholarly journals Construction and application of the 3D geo-hazard monitoring and early warning platform

2021 ◽  
Vol 13 (1) ◽  
pp. 1040-1052
Author(s):  
Dunlong Liu ◽  
Lei He ◽  
Qian Wu ◽  
Yan Gao ◽  
Bin Liu ◽  
...  
Keyword(s):  
Real Time ◽  
Information Management ◽  
Early Warning ◽  
Relevant Information ◽  
Geographical Information ◽  
Monitoring Data ◽  
Real Time Monitoring ◽  
Uniform Resource Identifier ◽  
Service Architecture ◽  
Monitoring And Early Warning

Abstract As geo-hazard monitoring data increases in category and size, conventional geo-hazard information management systems, without a unified integration framework and visualized data display, are unable to satisfy the urgent needs of geo-hazard information management. Representational State Transfer (REST), a resource-centered service architecture, abstracts data and services into resources for unified Uniform Resource Identifier access, enabling it to take full advantage of HTTP with great flexibility and scalability. Based on the REST service architecture, this paper constructs a 3D geo-hazard monitoring and early warning platform with sound service compatibility and scalability by integrating geographical information, real-time monitoring data, and early warning models into the 3D Digital Earth framework. The platform displays topography, stratum lithology, and relevant information, as well as real-time monitoring data in a 3D visual, and provides early warning services for geo-hazards through access to real-time early warning models. As a result, it is capable of providing comprehensive information management, monitoring, and early warning of multiple geo-hazards, aiding decision-making in disaster prevention and mitigation, and enhancing the information level of geo-hazard prevention and mitigation work.

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