DEVELOPMENT OF THE REAL-TIME RIVER STAGE PREDICTION METHOD USING DEEP LEARNING

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.

Download Full-text

A prediction method for the real-time remaining useful life of wind turbine bearings based on the Wiener process

Renewable Energy ◽

10.1016/j.renene.2018.04.033 ◽

2018 ◽

Vol 127 ◽

pp. 452-460 ◽

Cited By ~ 24

Author(s):

Yaogang Hu ◽

Hui Li ◽

Pingping Shi ◽

Zhaosen Chai ◽

Kun Wang ◽

...

Keyword(s):

Wind Turbine ◽

Real Time ◽

Wiener Process ◽

Prediction Method ◽

Remaining Useful Life ◽

The Real ◽

Useful Life

Download Full-text

Real-Time Data Filling and Automatic Retrieval Algorithm of Road Traffic Based on Deep-Learning Method

Symmetry ◽

10.3390/sym13010001 ◽

2020 ◽

Vol 13 (1) ◽

pp. 1

Author(s):

Jie Zhu ◽

Weixiang Xu

Keyword(s):

Deep Learning ◽

Real Time ◽

Road Traffic ◽

Retrieval Algorithm ◽

Target Area ◽

Learning Method ◽

Time Data ◽

The Real ◽

Automatic Retrieval ◽

Real Time Data

In order to enhance the real-time and retrieval performance of road traffic data filling, a real-time data filling and automatic retrieval algorithm based on the deep-learning method is proposed. In image detection, the depth representation is extracted according to the detection target area of a general object. The local invariant feature is extracted to describe local attributes in the region, and it is fused with depth representation to complete the real-time data filling of road traffic. According to the results of the database enhancement, the retrieval results of the deep representation level are reordered. In the index stage, unsupervised feature updating is realized by neighborhood information to improve the performance of a feature retrieval. The experimental results show that the proposed method has high recall and precision, a short retrieval time and a low running cost.

Download Full-text

The Real-Time Road Traffic Signal Light Assignment Strategy Prediction Based on Deep Learning

CICTP 2019 ◽

10.1061/9780784482292.287 ◽

2019 ◽

Author(s):

Dong Wei Xu ◽

He Gao ◽

Peng Peng ◽

Hai Feng Guo ◽

Qi Xuan

Keyword(s):

Deep Learning ◽

Real Time ◽

Road Traffic ◽

Traffic Signal ◽

The Real ◽

Signal Light ◽

Assignment Strategy

Download Full-text

Real-Time Detection and Location of Potted Flowers Based on a ZED Camera and a YOLO V4-Tiny Deep Learning Algorithm

Horticulturae ◽

10.3390/horticulturae8010021 ◽

2021 ◽

Vol 8 (1) ◽

pp. 21

Author(s):

Jizhang Wang ◽

Zhiheng Gao ◽

Yun Zhang ◽

Jing Zhou ◽

Jianzhi Wu ◽

...

Keyword(s):

Deep Learning ◽

Real Time ◽

Point Cloud ◽

Learning Algorithm ◽

Absolute Error ◽

Frame Rate ◽

3D Point Cloud ◽

The Real ◽

Deep Learning Algorithm ◽

The Mean

In order to realize the real-time and accurate detection of potted flowers on benches, in this paper we propose a method based on the ZED 2 stereo camera and the YOLO V4-Tiny deep learning algorithm for potted flower detection and location. First, an automatic detection model of flowers was established based on the YOLO V4-Tiny convolutional neural network (CNN) model, and the center points on the pixel plane of the flowers were obtained according to the prediction box. Then, the real-time 3D point cloud information obtained by the ZED 2 camera was used to calculate the actual position of the flowers. The test results showed that the mean average precision (MAP) and recall rate of the training model was 89.72% and 80%, respectively, and the real-time average detection frame rate of the model deployed under Jetson TX2 was 16 FPS. The results of the occlusion experiment showed that when the canopy overlap ratio between the two flowers is more than 10%, the recognition accuracy will be affected. The mean absolute error of the flower center location based on 3D point cloud information of the ZED 2 camera was 18.1 mm, and the maximum locating error of the flower center was 25.8 mm under different light radiation conditions. The method in this paper establishes the relationship between the detection target of flowers and the actual spatial location, which has reference significance for the machinery and automatic management of potted flowers on benches.

Download Full-text

Smart Surveillance System Using Computer Vision and Internet of Things

Journal of Computational and Theoretical Nanoscience ◽

10.1166/jctn.2020.8631 ◽

2020 ◽

Vol 17 (1) ◽

pp. 68-73

Author(s):

M. Hemaanand ◽

V. Sanjay Kumar ◽

R. Karthika

Keyword(s):

Deep Learning ◽

Internet Of Things ◽

Real Time ◽

Tracking System ◽

Single Shot ◽

Stream Data ◽

Advanced Technique ◽

The Real ◽

Smart Surveillance ◽

Bounding Boxes

With the evolution of technology ensuring people for their safety and security all around the time constantly is a big challenge. We propose an advanced technique based on deep learning and artificial intelligence platform that can monitor the people, their homes and their surroundings providing them a quantifiable increase in security. We have surveillance cameras in our homes for video capture as well as security purposes. Our proposed technique is to detect and classify as well as inform the user if there is any breach in security of the classified object using the cameras by implementing deep learning techniques and the technology of internet of things. It can serve as a perimeter monitoring and intruder alert system in smart surveillance environment. This paper provides a well-defined structure for live stream data analysis. It overcomes the challenge of static closed circuit cameras television as it serves as a motion based tracking system and monitors events in real time to ensure activities are limited to specific persons within authorized areas. It has the advantage of creating multiple bounding boxes to track down the objects which could be any living or non-living thing based on the trained modules. The trespasser or intruder can be efficiently detected using the CCTV camera surveillance which is being supported by the real-time object classifier algorithm at the intermediate module. The proposed method is mainly supported by the real time object detection and classification which is implemented using Mobile Net and Single shot detector.

Download Full-text