Microwave Propagation in Maritime Environments

2020 ◽  
Vol 54 (2) ◽  
pp. 17-24
Author(s):  
Arnaud Disant ◽  
Frederic Dias
Keyword(s):  
Real Time ◽  
Measurement Data ◽  
Satellite Communications ◽  
Breaking Waves ◽  
Enabling Technology ◽  
Microwave Propagation ◽  
Microwave Transmission ◽  
Simple Question ◽  
Data Connection ◽  
Data Transferring

Abstract A simple question that arises when dealing with maritime communications is: How would one offload large quantities of data from sea to shore and vice versa if one cannot use conventional solutions such as satellite communications or cellular data? In this note, we describe the first prototype solutions that were produced. They gave rise to SeaFi, which has become an enabling technology in the context of oceanic and coastal research. Measurements at sea are key to scientific research projects such as, for example, HIGHWAVE, a recently started ERC Advanced Grant project that relies partly on the possibility of measuring breaking waves in real time. Since the bottleneck is the real-time transmission of data, transferring measurement data at sea using SeaFi instead of using conventional satellite communications or a cellular data connection quickly became an evidence. To assess the resilience of SeaFi, a series of offshore experiments were performed from May to July 2018. Those experiments led on June 6, 2018, to a world record for the longest wireless microwave transmission at sea between a moving ship and a lighthouse, using the SeaFi communication system. In addition, the proposed solution also promises a better future for lighthouses around the world that are now gradually falling into disuse. This breakthrough in maritime telecommunications could change the way scientific researchers retrieve data in real time at sea in the coming years.

Real-time Approximation of Photometric Polygonal Lights

2020 ◽  
Vol 3 (1) ◽  
pp. 1-18
Author(s):  
Christian Luksch ◽  
Lukas Prost ◽  
Michael Wimmer
Keyword(s):  
Real Time ◽  
Specular Reflection ◽  
Near Field ◽  
Measurement Data ◽  
Data Sets ◽  
Photometric Measurement ◽  
Integration Technique ◽  
Time Approximation ◽  
Light Emitter ◽  
Selection Of

We present a real-time rendering technique for photometric polygonal lights. Our method uses a numerical integration technique based on a triangulation to calculate noise-free diffuse shading. We include a dynamic point in the triangulation that provides a continuous near-field illumination resembling the shape of the light emitter and its characteristics. We evaluate the accuracy of our approach with a diverse selection of photometric measurement data sets in a comprehensive benchmark framework. Furthermore, we provide an extension for specular reflection on surfaces with arbitrary roughness that facilitates the use of existing real-time shading techniques. Our technique is easy to integrate into real-time rendering systems and extends the range of possible applications with photometric area lights.

scholarly journals Online Simulation of Nuclear Power Plant Primary Systems

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Hongyun Xie ◽  
Haixia Gu ◽  
Chao Lu ◽  
Jialin Ping
Keyword(s):  
Information System ◽  
Power Plant ◽  
Nuclear Power Plant ◽  
Real Time ◽  
Nuclear Power ◽  
Measurement Data ◽  
Reactor Core ◽  
Power Industry ◽  
Nuclear Power Industry ◽  
Online Simulation

Real-time Simulation (RTS) has long been used in the nuclear power industry for operator training and engineering purposes. And, online simulation (OLS) is based on RTS and with connection to the plant information system to acquire the measurement data in real time for calibrating the simulation models and following plant operation, for the purpose of analyzing plant events and providing indicative signs of malfunctioning. OLS has been applied in certain industries to improve safety and efficiency. However, it is new to the nuclear power industry. A research project was initiated to implement OLS to assist operators in certain critical nuclear power plant (NPP) operations to avoid faulty conditions. OLS models were developed to simulate the reactor core physics and reactor/steam generator thermal hydraulics in real time, with boundary conditions acquired from plant information system, synchronized in real time. The OLS models then were running in parallel with recorded plant events to validate the models, and the results are presented.

Dynamic Measurement of Spatial Attitude at Bottom Rotating Drillstring: Simulation, Experimental, and Field Test

2015 ◽  
Vol 138 (2) ◽  
Author(s):  
Qilong Xue ◽  
Ruihe Wang ◽  
Baolin Liu ◽  
Leilei Huang
Keyword(s):  
Real Time ◽  
Measurement Errors ◽  
Time Window ◽  
Measurement Data ◽  
Movement Patterns ◽  
Dynamic Measurement ◽  
Drilling Process ◽  
Stick Slip ◽  
Actual Measurement ◽  
Gas Drilling

In the oil and gas drilling engineering, measurement-while-drilling (MWD) system is usually used to provide real-time monitoring of the position and orientation of the bottom hole. Particularly in the rotary steerable drilling technology and application, it is a challenge to measure the spatial attitude of the bottom drillstring accurately in real time while the drillstring is rotating. A set of “strap-down” measurement system was developed in this paper. The triaxial accelerometer and triaxial fluxgate were installed near the bit, and real-time inclination and azimuth can be measured while the drillstring is rotating. Furthermore, the mathematical model of the continuous measurement was established during drilling. The real-time signals of the accelerometer and the fluxgate sensors are processed and analyzed in a time window, and the movement patterns of the drilling bit will be observed, such as stationary, uniform rotation, and stick–slip. Different signal processing methods will be used for different movement patterns. Additionally, a scientific approach was put forward to improve the solver accuracy benefit from the use of stick–slip vibration phenomenon. We also developed the Kalman filter (KF) to improve the solver accuracy. The actual measurement data through drilling process verify that the algorithm proposed in this paper is reliable and effective and the dynamic measurement errors of inclination and azimuth are effectively reduced.

Dynamic Data-Driven Spatiotemporal System Behavior Prediction With Simulations and Sensor Measurement Data

2018 ◽  
Author(s):  
Xiangxue Zhao ◽  
Shapour Azarm ◽  
Balakumar Balachandran
Keyword(s):  
Dynamical System ◽  
Real Time ◽  
Measurement Data ◽  
Data Driven ◽  
Sensor Data ◽  
Model Parameters ◽  
High Fidelity ◽  
System Behavior ◽  
Sensor Measurement

Online prediction of dynamical system behavior based on a combination of simulation data and sensor measurement data has numerous applications. Examples include predicting safe flight configurations, forecasting storms and wildfire spread, estimating railway track and pipeline health conditions. In such applications, high-fidelity simulations may be used to accurately predict a system’s dynamical behavior offline (“non-real time”). However, due to the computational expense, these simulations have limited usage for online (“real-time”) prediction of a system’s behavior. To remedy this, one possible approach is to allocate a significant portion of the computational effort to obtain data through offline simulations. The obtained offline data can then be combined with online sensor measurements for online estimation of the system’s behavior with comparable accuracy as the off-line, high-fidelity simulation. The main contribution of this paper is in the construction of a fast data-driven spatiotemporal prediction framework that can be used to estimate general parametric dynamical system behavior. This is achieved through three steps. First, high-order singular value decomposition is applied to map high-dimensional offline simulation datasets into a subspace. Second, Gaussian processes are constructed to approximate model parameters in the subspace. Finally, reduced-order particle filtering is used to assimilate sparsely located sensor data to further improve the prediction. The effectiveness of the proposed approach is demonstrated through a case study. In this case study, aeroelastic response data obtained for an aircraft through simulations is integrated with measurement data obtained from a few sparsely located sensors. Through this case study, the authors show that along with dynamic enhancement of the state estimates, one can also realize a reduction in uncertainty of the estimates.

The Design of Gluing Robot Control System Based on CAN Bus

2014 ◽  
Vol 1006-1007 ◽  
pp. 627-630 ◽  
Author(s):  
Xu Dong Yang
Keyword(s):  
Control System ◽  
Real Time ◽  
Software Design ◽  
Robot Control ◽  
Can Bus ◽  
System Structure ◽  
Reliable Control ◽  
The Real ◽  
Data Transferring ◽  
Robot Control System

CAN bus was used as the data transferring channels in the two–level controllers, and the real-time,dexterity,expansibility and security for the Gluing control system based on CAN bus can be improved obviously.The system structure, principle and software design were introduced.The experiment shows that it is a reliable control system and it can meet the requirements of automatic gluing tasks.

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.

Research on Real-Time Measuring Method of Dynamic Deformation Based on Machine Vision and its Application

2011 ◽  
Vol 130-134 ◽  
pp. 3572-3576
Author(s):  
Li Zong Lin ◽  
Xiao Peng Ni ◽  
Luo Shan Zhou ◽  
Zhi Qin Qian
Keyword(s):  
Fatigue Strength ◽  
Machine Vision ◽  
Real Time ◽  
Dynamic Deformation ◽  
Measurement Data ◽  
Measuring Method ◽  
Strength Test ◽  
Time Deformation ◽  
Vision Measurement ◽  
Searching Method

Dynamic deformation measurement of machine parts in fatigue strength test is studied by using machine vision technique. Considering the uncertainty of parts surface, we adopt circular mark to locate the object profile in order to obtain high quality images. Through some image pre-processing with linear filtering, continuous contour searching method and circular detection based on random Hough transform (RHT), the real-time deformation can be measured with image characteristic parameters. In the practical application, the deformation of the loaded bicycle handle-bar is calculated. The test results show that the machine vision measurement is very effective; measurement resolution attains 0.1mm/pixel; the discrete degree of measurement data is low and the system meets the requirement of real-time measurement. The study proves that the measurement method of dynamic deformation based on machine vision is feasible, which can give some help for fatigue strength test of machine part and other structure deformation.

Artificial Intelligence and Data Analytics for Virtual Flow Metering

2021 ◽  
Author(s):  
Anton Gryzlov ◽  
Liliya Mironova ◽  
Sergey Safonov ◽  
Muhammad Arsalan
Keyword(s):  
Machine Learning ◽  
Multiphase Flow ◽  
Real Time ◽  
Production Control ◽  
Measurement Data ◽  
Sufficient Accuracy ◽  
Operating Conditions ◽  
Machine Learning Techniques ◽  
Flow Metering ◽  
Oil Gas

Abstract Modern challenges in reservoir management have recently faced new opportunities in production control and optimization strategies. These strategies in turn rely on the availability of monitoring equipment, which is used to obtain production rates in real-time with sufficient accuracy. In particular, a multiphase flow meter is a device for measuring the individual rates of oil, gas and water from a well in real-time without separating fluid phases. Currently, there are several technologies available on the market but multiphase flow meters generally incapable to handle all ranges of operating conditions with satisfactory accuracy in addition to being expensive to maintain. Virtual Flow Metering (VFM) is a mathematical technique for the indirect estimation of oil, gas and water flowrates produced from a well. This method uses more readily available data from conventional sensors, such as downhole pressure and temperature gauges, and calculates the multiphase rates by combining physical multiphase models, various measurement data and an optimization algorithm. In this work, a brief overview of the virtual metering methods is presented, which is followed by the application of several advanced machine-learning techniques for a specific case of multiphase production monitoring in a highly dynamic wellbore. The predictive capabilities of different types of machine learning instruments are explored using a model simulated production data. Also, the effect of measurement noise on the quality of estimates is considered. The presented results demonstrate that the data-driven methods are very capable to predict multiphase flow rates with sufficient accuracy and can be considered as a back-up solution for a conventional multiphase meter.

Real-time monitoring hypoxia at high altitudes using electrical bioimpedance technique: an animal experiment

2020 ◽  
Author(s):  
Lin Yang ◽  
Meng Dai ◽  
Qinglin Cao ◽  
Shuai Ding ◽  
Zhanqi Zhao ◽  
...  
Keyword(s):  
Real Time ◽  
Measurement Data ◽  
Area Under The Curve ◽  
Hypoxic Condition ◽  
Control Group ◽  
Characteristic Analysis ◽  
Non Invasive ◽  
Electrical Bioimpedance ◽  
High Concordance ◽  
Sensitivity Specificity

Hypoxia poses a serious threat to pilots. The aim of the study was to examine the efficacy of electrical bioimpedance (EBI) in detecting the onset of hypoxia in real time in a rabbit hypoxia model. Thirty-two New Zealand rabbits were divided equally into four groups (control group and 3 hypoxia groups, i.e. mild, moderate and severe). Hypoxia was induced by simulating various altitudes in the hypobaric oxygen chamber (3000 m, 5000 m and 8000 m). Both cerebral impedance and blood oxygen (SaO2) were monitored continuously. Results showed that the cerebral impedance increased immediately during the period of increasing altitude and decreased quickly to the initial baseline at the phase of descending altitude. Moreover, the change of cerebral impedance in mild hypoxia group (3000 m) is significantly smaller than those in the other two groups (5000 m and 8000 m, P<0.05). The changes of cerebral impedance and SaO2 were significantly correlated based on the total of measurement data (R2=0.628, P<0.001). Further, the agreement analysis performed with Bland-Altman and standardized residual plots exhibited high concordance between cerebral impedance and SaO2. Receiver operator characteristic analysis manifested that the sensitivity, specificity and area under the curve using cerebral impedance for changes of SaO2 >10% were 0.735, 0.826 and 0.845, respectively. These findings demonstrated that EBI could sensitively and accurately monitor changes of cerebral impedance induced by hypoxia, which might provide a potential tool for the real-time and non-invasive monitoring of hypoxic condition of pilots in flight for early identification of hypoxia.

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