Applying a deep learning enhanced public warning system to deal with COVID-19

Recently, developing countries have steadily been pushing for the construction of stream-oriented smart cities, breaking away from the existing old-town-centered development in the past. Due to the accelerating effects of climate change along with such urbanization, it is imperative for urban rivers to establish a flood warning system that can predict the amount of high flow rates of accuracy in engineering, compared to using the existing Computational Fluid Dynamics (CFD) models for disaster prevention. In this study, in the case of streams where missing data existed or only small observations were obtained, the variation in flow rates could be predicted with only the appropriate deep learning models, using only limited time series flow data. In addition, the selected deep learning model allowed the minimum number of input learning data to be determined. In this study, the time series flow rates were predicted by applying the deep learning models to the Han River, which is a highly urbanized stream that flows through the capital of Korea, Seoul and has a large seasonal variation in the flow rate. The deep learning models used are Convolution Neural Network (CNN), Simple Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM) and Gated Recurrent Unit (GRU). Sequence lengths for time series runoff data were determined first to assess the accuracy and applicability of the deep learning models. By analyzing the forecast results of the outflow data of the Han River, sequence length for 14 days was appropriate in terms of the predicted accuracy of the model. In addition, the GRU model is effective for deep learning models that use time series data of the region with large fluctuations in flow rates, such as the Han River. Furthermore, through this study, it was possible to propose the minimum number of training data that could provide flood warning system with an effective flood forecasting system although the number of input data such as flow rates secured in new towns developed around rivers was insufficient.

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Intelligent Vehicle Collision Warning System Based on a Deep Learning Approach

2018 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW) ◽

10.1109/icce-china.2018.8448556 ◽

2018 ◽

Cited By ~ 1

Author(s):

Yeong-Kang Lai ◽

Yu-Hau Huang ◽

Thomas Schumann

Keyword(s):

Deep Learning ◽

Warning System ◽

Intelligent Vehicle ◽

Learning Approach ◽

Collision Warning ◽

Vehicle Collision

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Deep-Learning and Vibration-Based System for Wear Size Estimation of Railway Switches and Crossings

Sensors ◽

10.3390/s21155217 ◽

2021 ◽

Vol 21 (15) ◽

pp. 5217

Author(s):

Taoufik Najeh ◽

Jan Lundberg ◽

Abdelfateh Kerrouche

Keyword(s):

Deep Learning ◽

Short Term Memory ◽

Early Stage ◽

Warning System ◽

Weather Conditions ◽

Size Estimation ◽

Vibration Data ◽

University Of Technology ◽

Railway Infrastructure ◽

Processing Techniques

The switch and crossing (S&C) is one of the most important parts of the railway infrastructure network due to its significant influence on traffic delays and maintenance costs. Two central questions were investigated in this paper: (I) the first question is related to the feasibility of exploring the vibration data for wear size estimation of railway S&C and (II) the second one is how to take advantage of the Artificial Intelligence (AI)-based framework to design an effective early-warning system at early stage of S&C wear development. The aim of the study was to predict the amount of wear in the entire S&C, using medium-range accelerometer sensors. Vibration data were collected, processed, and used for developing accurate data-driven models. Within this study, AI-based methods and signal-processing techniques were applied and tested in a full-scale S&C test rig at Lulea University of Technology to investigate the effectiveness of the proposed method. A real-scale railway wagon bogie was used to study different relevant types of wear on the switchblades, support rail, middle rail, and crossing part. All the sensors were housed inside the point machine as an optimal location for protection of the data acquisition system from harsh weather conditions such as ice and snow and from the ballast. The vibration data resulting from the measurements were used to feed two different deep-learning architectures, to make it possible to achieve an acceptable correlation between the measured vibration data and the actual amount of wear. The first model is based on the ResNet architecture where the input data are converted to spectrograms. The second model was based on a long short-term memory (LSTM) architecture. The proposed model was tested in terms of its accuracy in wear severity classification. The results show that this machine learning method accurately estimates the amount of wear in different locations in the S&C.

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AnnaData: Design and Development of a Robust Multi-sensor Early Warning System for Bacterial Blight Detection in Rice Crop using Deep Learning Techniques

2021 IEEE Conference on Technologies for Sustainability (SusTech) ◽

10.1109/sustech51236.2021.9467440 ◽

2021 ◽

Author(s):

Arup K. Mukherjee ◽

Santosh Kesavan ◽

Soumyprakash Das

Keyword(s):

Deep Learning ◽

Early Warning ◽

Early Warning System ◽

Bacterial Blight ◽

Warning System ◽

Rice Crop ◽

Design And Development ◽

Learning Techniques

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Innovative Research of Trajectory Prediction Algorithm Based on Deep Learning in Car Network Collision Detection and Early Warning System

Mobile Information Systems ◽

10.1155/2021/3773688 ◽

2021 ◽

Vol 2021 ◽

pp. 1-8

Author(s):

Rongxia Wang ◽

Malik Bader Alazzam ◽

Fawaz Alassery ◽

Ahmed Almulihi ◽

Marvin White

Keyword(s):

Deep Learning ◽

False Alarm ◽

False Alarm Rate ◽

Early Warning ◽

Collision Detection ◽

Early Warning System ◽

Warning System ◽

Early Warning Systems ◽

Trajectory Prediction ◽

Activation Area

Predicting the trajectories of neighboring vehicles is essential to evade or mitigate collision with traffic participants. However, due to inadequate previous information and the uncertainty in future driving maneuvers, trajectory prediction is a difficult task. Recently, trajectory prediction models using deep learning have been addressed to solve this problem. In this study, a method of early warning is presented using fuzzy comprehensive evaluation technique, which evaluates the danger degree of the target by comprehensively analyzing the target’s position, horizontal and vertical distance, speed of the vehicle, and the time of the collision. Because of the high false alarm rate in the early warning systems, an early warning activation area is established in the system, and the target state judgment module is triggered only when the target enters the activation area. This strategy improves the accuracy of early warning, reduces the false alarm rate, and also speeds up the operation of the early warning system. The proposed system can issue early warning prompt information to the driver in time and avoid collision accidents with accuracy up to 96%. The experimental results show that the proposed trajectory prediction method can significantly improve the vehicle network collision detection and early warning system.

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Deep learning-based production assists water quality warning system for reverse osmosis plants

H2Open Journal ◽

10.2166/h2oj.2020.059 ◽

2020 ◽

Author(s):

K. Udayakumar ◽

N. P. Subiramaniyam

Keyword(s):

Water Quality ◽

Time Series ◽

Deep Learning ◽

Reverse Osmosis ◽

Production Line ◽

Warning System ◽

Control Methods ◽

Classification Methods ◽

Corrective Procedures ◽

Quality Features

Abstract Classifying water quality irregularities in reverse osmosis (RO) production plants requires suitable systems to provide intelligent warnings to the operators or supervisors who are engaged in executing corrective procedures applicable to production. The suggested deep learning methods are of utmost importance to identify at once variations in water quality irregularities in plants through competent classification methods, thereby enabling a reduction of burden for operators. In this paper, two types of LSTM-CNN based classification techniques are suggested to classify water quality features temporally into grades based on corrective actions that classify irregularity conditions of water quality on the basis of corrections. Distinct control methods are used for experiment to find water quality irregularities from variables, namely, pH, TDS, ORP, and EC, which aim to assist the production line. This proposed method enables automatic diagnosis and warning system about water quality in RO plants. For classification, LSTM-CNN was trained with data recorded from eight plants of west and north parts of Chennai region. This research is meant to demonstrate particularly the top-level classification job for quality alerts. The features obtained from 4,096 time series array data using LSTM-CNNs achieved sensitivity to 97% and accuracy to 98%.

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