Combined LF-NMR and Artificial Intelligence for Continuous Real-Time Monitoring of Carrot in Microwave Vacuum Drying

2019 ◽  
Vol 12 (4) ◽  
pp. 551-562 ◽  
Author(s):  
Qing Sun ◽  
Min Zhang ◽  
Arun S. Mujumdar ◽  
Peiqiang Yang
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
Vacuum Drying ◽  
Real Time Monitoring ◽  
Microwave Vacuum Drying ◽  
Lf Nmr

Artificial intelligence model for real-time monitoring of ideological and political teaching system

2020 ◽  
pp. 1-10
Author(s):  
Ying Luo
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
Problem Behaviors ◽  
Control Experiment ◽  
Real Time Monitoring ◽  
Model Function ◽  
Monitoring Equipment ◽  
Information Monitoring ◽  
Function Modules ◽  
Artificial Intelligence Technology

In ideological and political teaching, students have more serious problem behaviors in the classroom, including distracted, dazed, inattentive, and sleeping. In order to improve the efficiency of ideological and political teaching, based on artificial intelligence technology, this paper constructs a real-time monitoring system for ideological and political classrooms based on artificial intelligence algorithms, and builds model function modules according to the actual needs of ideological and political teaching monitoring. Moreover, this study makes reasonable calculations on the information monitoring and information transmission parts and installs a different number of monitoring equipment in different fixed locations according to the needs of signal monitoring. In addition, this paper designs a control experiment to study the system performance and verify the parameters from multiple aspects. The research results show that the system model constructed in this paper is stable in ideological and political teaching and has certain effects.

scholarly journals Intelligent detection of flavor changes in ginger during microwave vacuum drying based on LF-NMR

2019 ◽  
Vol 119 ◽  
pp. 417-425 ◽  
Author(s):  
Yanan Sun ◽  
Min Zhang ◽  
Bhesh Bhandari ◽  
Peiqiang Yang
Keyword(s):  
Vacuum Drying ◽  
Microwave Vacuum Drying ◽  
Intelligent Detection ◽  
Lf Nmr

IoT-based Flood Depth Sensors in Artificial Intelligent Urban Flood Warning Systems

2020 ◽  
Author(s):  
Li-Chiu Chang ◽  
Fi-John Chang
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
Value Added ◽  
Flood Forecasting ◽  
Flood Inundation ◽  
Disaster Prevention ◽  
Real Time Monitoring ◽  
Flood Depth ◽  
Flood Warning ◽  
On Line

<p>In the face of increasingly flood disasters, on-line regional flood inundation forecasting in urban areas is vital for city flood management, while it remains a significant challenge because of the complex interactions and disruptions associated with highly uncertain hydro-meteorological variables and the lack of high-resolution hydro-geomorphological data. Effective on-line flood forecasting models through the rapid dissemination of inundation information regarding threatened areas deserve to develop appropriate technologies for early warning and disaster prevention. Artificial Intelligence (AI) becomes one of the popular techniques in the study of flood forecasts in the last decades. We apply the AI techniques with the newly implemented IoT-based real-time monitoring flood depth data to build an urban AI flood warning system. The AI system integrates the self-organizing feature mapping networks (SOM) with the recurrent nonlinear autoregressive with exogenous inputs network (R-NARX) for modelling the regional flooding prediction. The proposed AI model with the IoT-based real-time monitoring flood depth datasets can increase the value-added application of diversified disaster prevention information and improve the accuracy of flood forecasting. We develop an on-line correction algorithm for continuously learning and correcting model’s parameters, automatic operation modules, forecast results output modules, and web page display interface. The proposed AI system can provide the smart early flooding warnings in the urban area and help the Water Resources Agency to promote the intelligent water disaster prevention services.</p><p>Keywords:</p><p>Artificial Intelligence (AI); Artificial Neural Networks (ANN); Internet of Things (IoT); Regional flood inundation forecast; Spatial-temporal distribution</p>

LF-NMR online detection of water dynamics in apple cubes during microwave vacuum drying

2018 ◽  
Vol 36 (16) ◽  
pp. 2006-2015 ◽  
Author(s):  
Linlin Li ◽  
Min Zhang ◽  
Bhesh Bhandari ◽  
Lequn Zhou
Keyword(s):  
Water Dynamics ◽  
Vacuum Drying ◽  
Online Detection ◽  
Microwave Vacuum Drying ◽  
Lf Nmr

scholarly journals Real-Time Monitoring for Hydraulic States Based on Convolutional Bidirectional LSTM with Attention Mechanism

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7099
Author(s):  
Kyutae Kim ◽  
Jongpil Jeong
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Real Time ◽  
Short Term Memory ◽  
Attention Mechanism ◽  
Real Time Monitoring ◽  
Short Term ◽  
Term Memory ◽  
Anomalous Data ◽  
Long Short Term Memory

By monitoring a hydraulic system using artificial intelligence, we can detect anomalous data in a manufacturing workshop. In addition, by analyzing the anomalous data, we can diagnose faults and prevent failures. However, artificial intelligence, especially deep learning, needs to learn much data, and it is often difficult to get enough data at the real manufacturing site. In this paper, we apply augmentation to increase the amount of data. In addition, we propose real-time monitoring based on a deep-learning model that uses convergence of a convolutional neural network (CNN), a bidirectional long short-term memory network (BiLSTM), and an attention mechanism. CNN extracts features from input data, and BiLSTM learns feature information. The learned information is then fed to the sigmoid classifier to find out if it is normal or abnormal. Experimental results show that the proposed model works better than other deep-learning models, such as CNN or long short-term memory (LSTM).

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