scholarly journals A Haze Prediction Method Based on One-Dimensional Convolutional Neural Network

Atmosphere ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 1327
Author(s):  
Ziyan Zhang ◽  
Jiawei Tian ◽  
Weizheng Huang ◽  
Lirong Yin ◽  
Wenfeng Zheng ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Time Scale ◽  
Concentration Level ◽  
Prediction Method ◽  
Concentration Data ◽  
One Dimensional ◽  
The Public ◽  
The Past ◽  
Gated Recurrent Unit

In recent years, more and more people are paying close attention to the environmental problems in metropolitan areas and their harm to the human body. Among them, haze is the pollutant that people are most concerned about. The demand for a method to predict the haze level for the public and academics keeps rising. In order to predict the haze concentration on a time scale in hours, this study built a haze concentration prediction method based on one-dimensional convolutional neural networks. The gated recurrent unit method was used for comparison, which highlights the training speed of a one-dimensional convolutional neural network. In summary, the haze concentration data of the past 24 h are used as input and the haze concentration level on the next moment as output such that the haze concentration level on the time scale in hours can be predicted. Based on the results, the prediction accuracy of the proposed method is over 95% and can be used to support other studies on haze prediction.

scholarly journals Deep Learning Forecasts of Soil Moisture: Convolutional Neural Network and Gated Recurrent Unit Models Coupled with Satellite-Derived MODIS, Observations and Synoptic-Scale Climate Index Data

2021 ◽  
Vol 13 (4) ◽  
pp. 554
Author(s):  
A. A. Masrur Ahmed ◽  
Ravinesh C Deo ◽  
Nawin Raj ◽  
Afshin Ghahramani ◽  
Qi Feng ◽  
...  
Keyword(s):  
Neural Network ◽  
Soil Moisture ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Climate Index ◽  
Climate Indices ◽  
High Temporal Resolution ◽  
Synoptic Scale ◽  
Time Step ◽  
Gated Recurrent Unit

Remotely sensed soil moisture forecasting through satellite-based sensors to estimate the future state of the underlying soils plays a critical role in planning and managing water resources and sustainable agricultural practices. In this paper, Deep Learning (DL) hybrid models (i.e., CEEMDAN-CNN-GRU) are designed for daily time-step surface soil moisture (SSM) forecasts, employing the gated recurrent unit (GRU), complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and convolutional neural network (CNN). To establish the objective model’s viability for SSM forecasting at multi-step daily horizons, the hybrid CEEMDAN-CNN-GRU model is tested at 1st, 5th, 7th, 14th, 21st, and 30th day ahead period by assimilating a comprehensive pool of 52 predictor dataset obtained from three distinct data sources. Data comprise satellite-derived Global Land Data Assimilation System (GLDAS) repository a global, high-temporal resolution, unique terrestrial modelling system, and ground-based variables from Scientific Information Landowners (SILO) and synoptic-scale climate indices. The results demonstrate the forecasting capability of the hybrid CEEMDAN-CNN-GRU model with respect to the counterpart comparative models. This is supported by a relatively lower value of the mean absolute percentage and root mean square error. In terms of the statistical score metrics and infographics employed to test the final model’s utility, the proposed CEEMDAN-CNN-GRU models are considerably superior compared to a standalone and other hybrid method tested on independent SSM data developed through feature selection approaches. Thus, the proposed approach can be successfully implemented in hydrology and agriculture management.

scholarly journals Stamping Monitoring by Using an Adaptive 1D Convolutional Neural Network

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 262
Author(s):  
Chih-Yung Huang ◽  
Zaky Dzulfikri
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Template Matching ◽  
Health Condition ◽  
Vibration Measurement ◽  
One Dimensional ◽  
Stamping Process ◽  
Metalworking Industry ◽  
Increasing Demand ◽  
Wear Condition

Stamping is one of the most widely used processes in the sheet metalworking industry. Because of the increasing demand for a faster process, ensuring that the stamping process is conducted without compromising quality is crucial. The tool used in the stamping process is crucial to the efficiency of the process; therefore, effective monitoring of the tool health condition is essential for detecting stamping defects. In this study, vibration measurement was used to monitor the stamping process and tool health. A system was developed for capturing signals in the stamping process, and each stamping cycle was selected through template matching. A one-dimensional (1D) convolutional neural network (CNN) was developed to classify the tool wear condition. The results revealed that the 1D CNN architecture a yielded a high accuracy (>99%) and fast adaptability among different models.

scholarly journals A phase-error prediction method for coherent beam combining via convolutional neural network

Optik ◽  
2021 ◽  
pp. 167827
Author(s):  
Haolong Jia ◽  
Jing Zuo ◽  
Qiliang Bao ◽  
Chao Geng ◽  
Xinyang Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Phase Error ◽  
Prediction Method ◽  
Coherent Beam ◽  
Error Prediction ◽  
Coherent Beam Combining ◽  
Beam Combining
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