scholarly journals Monitoring and Analysis of Wave Characteristics during Pipeline End Termination Installation

Processes ◽  
2019 ◽  
Vol 7 (9) ◽  
pp. 569
Author(s):  
Duanfeng Han ◽  
Ting Cui ◽  
Lihao Yuan ◽  
Yingfei Zan ◽  
Zhaohui Wu
Keyword(s):  
Real Time ◽  
Wave Motion ◽  
Fourier Transforms ◽  
Classical Method ◽  
Suitable Model ◽  
Wave Data ◽  
Wave Characteristics ◽  
Environmental Monitoring System ◽  
Spreading Function ◽  
Marine Environmental

Pipeline end termination (PLET) installation is an essential part of offshore pipe-laying operation. Pipe-laying operations are sensitive to pipe-laying barge motion and marine environmental conditions. Monitoring the field environment can provide a reasonable basis for planning pipe-laying. Therefore, the measurement and analysis of sea wave motion is helpful for the control and operational safety of the pipeline and vessels. In this study, an environmental monitoring system was established to measure wave motion during PLET operation. Fourier transforms were used to process images that were acquired by ultra-high-frequency X-band marine radar to extract wave parameters. The resulting wave spectra, as measured each minute, were used to simulate real-time wave data and calculate wave characteristics and regressed wave frequency and direction spectrum throughout the PLET operation. The regressed frequency, spectral density, and direction spectra were compared with the theoretical spectra to evaluate their similarity and find the most similar spreading function in the operational area (the South China Sea). Gaussian fitting of real-time wave data was tested while using a classical method. The marginal distribution and joint density of the wave characteristics were estimated and then compared with theoretical distributions to find the most suitable model for improving marine environmental forecasting.

scholarly journals Real-Time Lossless Compression for Ultra-High-Density Synchrophasor and Point on Wave Data

2021 ◽  
pp. 1-1
Author(s):  
Chang Chen ◽  
Weikang Wang ◽  
Yin He ◽  
Lingwei Zhan ◽  
Yilu Liu
Keyword(s):  
Real Time ◽  
Lossless Compression ◽  
High Density ◽  
Wave Data

scholarly journals High-Bandpass Filters in Electrocardiography: Source of Error in the Interpretation of the ST Segment

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
F. Buendía-Fuentes ◽  
M. A. Arnau-Vives ◽  
A. Arnau-Vives ◽  
Y. Jiménez-Jiménez ◽  
J. Rueda-Soriano ◽  
...  
Keyword(s):  
Real Time ◽  
Fourier Transforms ◽  
Pass Filter ◽  
Discrete Fourier Transforms ◽  
Real Time Mode ◽  
Asymptomatic Patients ◽  
St Segment ◽  
Coronary Syndromes ◽  
Clinically Significant ◽  
High Pass

Introduction. Artifactual variations in the ST segment may lead to confusion with acute coronary syndromes. Objective. To evaluate how the technical characteristics of the recording mode may distort the ST segment. Material and Method. We made a series of electrocardiograms using different filter configurations in 45 asymptomatic patients. A spectral analysis of the electrocardiograms was made by discrete Fourier transforms, and an accurate recomposition of the ECG signal was obtained from the addition of successive harmonics. Digital high-pass filters of 0.05 and 0.5 Hz were used, and the resulting shapes were compared with the originals. Results. In 42 patients (93%) clinically significant alterations in ST segment level were detected. These changes were only seen in “real time mode” with high-pass filter of 0.5 Hz. Conclusions. Interpretation of the ST segment in “real time mode” should only be carried out using high-pass filters of 0.05 Hz.

Machine-Learning for the Prediction of Lost Circulation Events - Time Series Analysis and Model Evaluation

2021 ◽  
Author(s):  
Arturo Magana-Mora ◽  
Mohammad AlJubran ◽  
Jothibasu Ramasamy ◽  
Mohammed AlBassam ◽  
Chinthaka Gooneratne ◽  
...  
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Real Time ◽  
Large Scale ◽  
Model Comparison ◽  
Drilling Fluid ◽  
Sensor Data ◽  
False Alarms ◽  
Suitable Model ◽  
Lost Circulation

Abstract Objective/Scope. Lost circulation events (LCEs) are among the top causes for drilling nonproductive time (NPT). The presence of natural fractures and vugular formations causes loss of drilling fluid circulation. Drilling depleted zones with incorrect mud weights can also lead to drilling induced losses. LCEs can also develop into additional drilling hazards, such as stuck pipe incidents, kicks, and blowouts. An LCE is traditionally diagnosed only when there is a reduction in mud volume in mud pits in the case of moderate losses or reduction of mud column in the annulus in total losses. Using machine learning (ML) for predicting the presence of a loss zone and the estimation of fracture parameters ahead is very beneficial as it can immediately alert the drilling crew in order for them to take the required actions to mitigate or cure LCEs. Methods, Procedures, Process. Although different computational methods have been proposed for the prediction of LCEs, there is a need to further improve the models and reduce the number of false alarms. Robust and generalizable ML models require a sufficiently large amount of data that captures the different parameters and scenarios representing an LCE. For this, we derived a framework that automatically searches through historical data, locates LCEs, and extracts the surface drilling and rheology parameters surrounding such events. Results, Observations, and Conclusions. We derived different ML models utilizing various algorithms and evaluated them using the data-split technique at the level of wells to find the most suitable model for the prediction of an LCE. From the model comparison, random forest classifier achieved the best results and successfully predicted LCEs before they occurred. The developed LCE model is designed to be implemented in the real-time drilling portal as an aid to the drilling engineers and the rig crew to minimize or avoid NPT. Novel/Additive Information. The main contribution of this study is the analysis of real-time surface drilling parameters and sensor data to predict an LCE from a statistically representative number of wells. The large-scale analysis of several wells that appropriately describe the different conditions before an LCE is critical for avoiding model undertraining or lack of model generalization. Finally, we formulated the prediction of LCEs as a time-series problem and considered parameter trends to accurately determine the early signs of LCEs.

Intelligent Environmental Monitoring System Based on Multi-Sensor Data Technology

2020 ◽  
Vol 11 (4) ◽  
pp. 57-71
Author(s):  
Qiuxia Liu
Keyword(s):  
Data Fusion ◽  
Environmental Monitoring ◽  
Real Time ◽  
Monitoring System ◽  
Intelligent System ◽  
Evidence Synthesis ◽  
Environmental Parameters ◽  
Monitoring Data ◽  
Sensor Data ◽  
Environmental Monitoring System

Using multi-sensor data fusion technology, ARM technology, ZigBee technology, GPRS, and other technologies, an intelligent environmental monitoring system is studied and developed. The SCM STC12C5A60S2 is used to collect the main environmental parameters in real time intelligently. The collected data is transmitted to the central controller LPC2138 through the ZigBee module ATZGB-780S5, and then the collected data is transmitted to the management computer through the GPRS communication module SIM300; thus, the real-time processing and intelligent monitoring of the environmental parameters are realized. The structure of the system is optimized; the suitable fusion model of environmental monitoring parameters is established; the hardware and the software of the intelligent system are completed. Each sensor is set up synchronously at the end of environmental parameter acquisition. The method of different value detection is used to filter out different values. The authors obtain the reliability of the sensor through the application of the analytic hierarchy process. In the analysis and processing of parameters, they proposed a new data fusion algorithm by using the reliability, probability association algorithm, and evidence synthesis algorithm. Through this algorithm, the accuracy of environmental monitoring data and the accuracy of judging monitoring data are greatly improved.

scholarly journals OPC Protocol Application for Real-Time Carbon Monitoring System for Industrial Environment

2017 ◽  
Vol 7 (2) ◽  
pp. 1051 ◽  
Author(s):  
Mohd Faiz Rohani ◽  
Noor Azurati Ahmad ◽  
Shamsul Sahibuddin ◽  
Salwani Mohd Daud
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Ghg Emissions ◽  
Measurement Data ◽  
Sensor Data ◽  
Successful Implementation ◽  
Open Platform ◽  
Environmental Monitoring System ◽  
Normal Environment ◽  
Communications Protocol

Global warming is referred to the rise in average surface temperatures on earth primarily due to the Greenhouse Gases (GHG) emissions such as Carbon Dioxide (CO<sub>2</sub>). Monitoring the emissions, either direct or indirect from the industrial processes, is important to control or to minimize their impact on the environment. Most of the existing environmental monitoring system is being designed and developed for normal environment monitoring. Hence, the aim of this project is to develop industrial CO<sub>2 </sub>emission monitoring system which implements industrial Open Platform Communications (OPC) protocol in an embedded microcontroller. The software algorithm based on OPC data format has been designed and programmed into the Arduino microcontroller to interface the sensor data to any existing industrial OPC compliant Supervisory Control and Data Acquisition (SCADA) system<strong>. </strong>The system has been successfully tested in a lab with the suitable environment for real-time CO<sub>2 </sub>emissions measurement. The real-time measurement data has been shown in an industrial SCADA application which indicates successful implementation of the OPC communications protocol.

Approach to real time attack detection on the base of simulation and graph oriented modeling

2021 ◽  
Vol 7 ◽  
Author(s):  
V.A. Desnitsky ◽  
Keyword(s):  
Real Time ◽  
Software Tool ◽  
Attack Detection ◽  
Suitable Model ◽  
Distinctive Features ◽  
Detection Process ◽  
Detection Model ◽  
Security Incidents

The article presents an approach to detecting attacks in real time based on simulation and graph-oriented mod- eling. The detection process is performed in a mode close to real-time with the ability to promptly detect known types of security incidents. The distinctive features of the approach include the multidimensional nature of attack detection with the ability to select a specific type of simulation and graph-oriented attack detection model with their subsequent combination. In addition, within the practical part of the work, a software tool has been developed to select the most suitable model apparatus for detecting attacks of each type.

Computer Vision Applications of Self-Organizing Neural Networks

2013 ◽  
pp. 129-138
Author(s):  
José García-Rodríguez ◽  
Juan Manuel García-Chamizo ◽  
Sergio Orts-Escolano ◽  
Vicente Morell-Gimenez ◽  
José Antonio Serra-Pérez ◽  
...  
Keyword(s):  
Real Time ◽  
Network Models ◽  
Suitable Model ◽  
Surveillance Systems ◽  
Neural Network Models ◽  
Neural Gas ◽  
3D Data ◽  
Computer Vision Applications ◽  
Recognition Systems ◽  
Self Organizing

This chapter aims to address the ability of self-organizing neural network models to manage video and image processing in real-time. The Growing Neural Gas networks (GNG) with its attributes of growth, flexibility, rapid adaptation, and excellent quality representation of the input space makes it a suitable model for real time applications. A number of applications are presented, including: image compression, hand and medical image contours representation, surveillance systems, hand gesture recognition systems, and 3D data reconstruction.

scholarly journals Design and Simulation of a Wireless Sensor Network Greenhouse-Monitoring System Based on 3G Network Communication

2016 ◽  
Vol 12 (05) ◽  
pp. 48 ◽  
Author(s):  
Y. H. Zhou ◽  
J. G. Duan
Keyword(s):  
Wireless Sensor Network ◽  
Real Time ◽  
Sensor Network ◽  
Monitoring System ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Network Communication ◽  
Target Area ◽  
Wireless Communication Network ◽  
Environmental Monitoring System

A greenhouse provides a stable and suitable environment for the growth of plants. Temperature and humidity are closely related to plant growth. These factors directly affect the water content of plants and the quality of fruits. To solve the problems in the current monitoring system of greenhouse cultivation, such as complicated wiring, large node power consumption, and so on, this study proposes a wireless sensor network greenhouse-monitoring system based on third-generation network communication for the real-time monitoring of the temperature, humidity, light, and CO<sub>2</sub> concentration in a greenhouse. GS1011M is regarded as the core in developing wireless terminal nodes. PC software is used to build a real-time observation platform. Sensor data are received in real time through a wireless communication network to complete the monitoring of the target area. A simulation research is also conducted. Results show that the power dissipation of the greenhouse environmental monitoring system is low, its data accuracy is high, and its operation is stable.

Smart Environmental Monitoring System

2019 ◽  
Vol 10 (1) ◽  
pp. 43-54
Author(s):  
Karthik Sudhakaran Menon ◽  
Brinzel Rodrigues ◽  
Akash Prakash Barot ◽  
Prasad Avinash Gharat
Keyword(s):  
Air Pollution ◽  
Real Time ◽  
Urban Areas ◽  
Real Life ◽  
Cloud Service ◽  
Threshold Value ◽  
Time Data ◽  
Multiple Sensors ◽  
Alert Message ◽  
Environmental Monitoring System

In today's world, air pollution has become a common phenomenon everywhere, especially in the urban areas, air pollution is a real-life problem. In urban areas, the increased number of hydrocarbons and diesel vehicles and the presence of industrial areas at the outskirts of the major cities are the main causes of air pollution. The problem is seriously intense within the metropolitan cities. The governments around the world are taking measure in their capability. The main aim of this project is to develop a system which may monitor and measure pollutants in the air in real time, tell the quality of air and log real-time data onto a remote server (Cloud Service). If the value of the parameters exceeds the given threshold value, then an alert message is sent with the GPS coordinates to the registered number of the authority or person so necessary actions can be taken. The Arduino board connects with Thingspeak cloud service platform using ESP8266 Wi-Fi module. The device uses multiple sensors for monitoring the parameters of the air pollution like MQ-135, MQ-7, DHT-22, sound sensor, LCD.

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