Design and Development of a Drifting Buoy for Gathering Environmental Data

2014 ◽  
Vol 651-653 ◽  
pp. 417-421
Author(s):  
Xin Jun Gan ◽  
Yong Hua Chen ◽  
Yong Ping Xu ◽  
Tao Zuo Ni ◽  
Jing Bo Jiang ◽  
...  
Keyword(s):  
Real Time ◽  
Prediction Models ◽  
Water Mass ◽  
Environmental Data ◽  
Ocean Current ◽  
Station Network ◽  
Ground Station ◽  
Drifting Buoy ◽  
Main Components ◽  
Current Drift

Operational meteorologists and Oceanographers rely on real-time environmental data to run their numerical prediction models, even carry on the research. The ground station network is dense and the data of good quality, but there is not enough environmental data from the oceans, particularly in data-sparse areas not covered by commercial ships reporting environmental data. A drifting ocean buoy is described. The drifter consists of three main components: a surface float, a tether assembly and a dimensionally-stable drogue. It utilizes a drag structure which follows the water mass of the ocean as it flows in the form of the ocean current, and which also has an aerodynamically shaped low wind drag mast to minimize wind induced errors in ocean current drift measurements; the drag structure also being stable and resistant to heaving (pitch and roll) so as to maintain a mast carried antenna above the water even at high sea states.

scholarly journals The Ground Control Station Design For Can-Sized Satellite (CanSAT) System

2020 ◽  
Vol 1 (2) ◽  
pp. 76-82
Author(s):  
Noor Thuwaibah Abdul Razak ◽  
Huda A Majid ◽  
Faiz Asraf Saparuddin ◽  
Muhamad Fitry Abdul Jalil ◽  
Muhamad Shakry Jamaluddin Jalil ◽  
...  
Keyword(s):  
Real Time ◽  
Software Design ◽  
Lightweight Design ◽  
Time Data ◽  
Time System ◽  
Real Time System ◽  
Ground Station ◽  
Medical Supplies ◽  
Emergency Medical ◽  
Main Components

Nowadays, Natural disaster tragedy is now one of the world's biggest concerns. Can-sized satellite, MedSAT: Location-Aware CanSAT for On-Site Emergency Medical Supplies develop a platform for finding direction and accurately locating an emergency patient and providing emergency medical supplies such as bandages, antiseptic wipes, sterile gauze pads of various sizes, insulin, pills, syringe and antivenom, as well as real-time visual feed for medical diagnosis during and after landing. This project focuses on the design of MedSAT and provides a real-time system to capture MedSAT’s real-time data during descent. The objective of the real-time system is to improve the accuracy and location speed of MedSAT data collection which can provide readings of altitude, latitude and longitude to help MedSAT navigate to the patient location. Hardware design (flight controller, GPS module and telemetry kit), software design (Mission Planner) and real-time system (RTS) are the main components of this platform. In addition, the ground station was developed to communicate with users via wireless telemetry communication using MAVLink protocol. Based on the overall findings, MedSAT and ground station's compact and lightweight design was developed in search and rescue operations for emergency location.

A Review of the 2014 Gulf of Mexico Wave Glider® Field Program

2015 ◽  
Vol 49 (3) ◽  
pp. 64-71 ◽  
Author(s):  
Patrick J. Fitzpatrick ◽  
Yee Lau ◽  
Robert Moorhead ◽  
Adam Skarke ◽  
Daniel Merritt ◽  
...  
Keyword(s):  
Gulf Of Mexico ◽  
Real Time ◽  
Absolute Error ◽  
Lessons Learned ◽  
Environmental Data ◽  
Ocean Current ◽  
Tropical Cyclone Genesis ◽  
Current Profile ◽  
National Data ◽  
Wave Glider

AbstractSustained observations of oceanographic and atmospheric boundary layer conditions are imperative for the investigation of tropical cyclone genesis, for numerical model input to predict track and intensity, and in general, for many environmental monitoring needs. We present preliminary results of a Fall 2014 100-day deployment of Wave Glider platforms in the eastern Gulf of Mexico designed to dynamically collect surface weather, water temperature, wave, and ocean current profile data within tropical cyclones. Data were collected and retransmitted near real time through a Liquid Robotics interface to regional and national data portals such as the National Data Buoy Center, and secondarily also used by the private sector. Accomplishments include buoy loitering for validation exercises, data gap filling, platform redeployments, and an interception of the fringes of Tropical Storm Hanna. Preliminary buoy loitering assessments using bias and absolute error metrics showed reasonable agreement with buoys for atmospheric pressure, wave, and height-adjusted wind data but that the temperature hardware requires an improved sensor. A full assessment of the potential for the sustained collection and real-time dissemination of environmental data for Wave Glider platforms is presented including lessons learned.

Adaptive Signal Analysis and Interpretation for Real-time Intelligent Patient Monitoring

1994 ◽  
Vol 33 (01) ◽  
pp. 60-63 ◽  
Author(s):  
E. J. Manders ◽  
D. P. Lindstrom ◽  
B. M. Dawant
Keyword(s):  
Computer Architecture ◽  
Real Time ◽  
Data Abstraction ◽  
Monitoring Strategy ◽  
Intelligent Monitoring ◽  
Patient Status ◽  
On Line ◽  
Main Components ◽  
And Control ◽  
Adaptive Signal

Abstract:On-line intelligent monitoring, diagnosis, and control of dynamic systems such as patients in intensive care units necessitates the context-dependent acquisition, processing, analysis, and interpretation of large amounts of possibly noisy and incomplete data. The dynamic nature of the process also requires a continuous evaluation and adaptation of the monitoring strategy to respond to changes both in the monitored patient and in the monitoring equipment. Moreover, real-time constraints may imply data losses, the importance of which has to be minimized. This paper presents a computer architecture designed to accomplish these tasks. Its main components are a model and a data abstraction module. The model provides the system with a monitoring context related to the patient status. The data abstraction module relies on that information to adapt the monitoring strategy and provide the model with the necessary information. This paper focuses on the data abstraction module and its interaction with the model.

scholarly journals HGPT2: An ERA5-Based Global Model to Estimate Relative Humidity

2021 ◽  
Vol 13 (11) ◽  
pp. 2179
Author(s):  
Pedro Mateus ◽  
Virgílio B. Mendes ◽  
Sandra M. Plecha
Keyword(s):  
Water Vapor ◽  
Relative Humidity ◽  
Real Time ◽  
Prediction Models ◽  
Weather Prediction ◽  
Precipitable Water ◽  
Precipitable Water Vapor ◽  
Global Navigation Satellite Systems ◽  
International Gnss Service ◽  
Weighted Mean Temperature

The neutral atmospheric delay is one of the major error sources in Space Geodesy techniques such as Global Navigation Satellite Systems (GNSS), and its modeling for high accuracy applications can be challenging. Improving the modeling of the atmospheric delays (hydrostatic and non-hydrostatic) also leads to a more accurate and precise precipitable water vapor estimation (PWV), mostly in real-time applications, where models play an important role, since numerical weather prediction models cannot be used for real-time processing or forecasting. This study developed an improved version of the Hourly Global Pressure and Temperature (HGPT) model, the HGPT2. It is based on 20 years of ERA5 reanalysis data at full spatial (0.25° × 0.25°) and temporal resolution (1-h). Apart from surface air temperature, surface pressure, zenith hydrostatic delay, and weighted mean temperature, the updated model also provides information regarding the relative humidity, zenith non-hydrostatic delay, and precipitable water vapor. The HGPT2 is based on the time-segmentation concept and uses the annual, semi-annual, and quarterly periodicities to calculate the relative humidity anywhere on the Earth’s surface. Data from 282 moisture sensors located close to GNSS stations during 1 year (2020) were used to assess the model coefficients. The HGPT2 meteorological parameters were used to process 35 GNSS sites belonging to the International GNSS Service (IGS) using the GAMIT/GLOBK software package. Results show a decreased root-mean-square error (RMSE) and bias values relative to the most used zenith delay models, with a significant impact on the height component. The HGPT2 was developed to be applied in the most diverse areas that can significantly benefit from an ERA5 full-resolution model.

Stochastic investigation of daily air temperature extremes from a global ground station network

2021 ◽  
Author(s):  
Konstantinos-Georgios Glynis ◽  
Theano Iliopoulou ◽  
Panayiotis Dimitriadis ◽  
Demetris Koutsoyiannis
Keyword(s):  
Air Temperature ◽  
Temperature Extremes ◽  
Station Network ◽  
Ground Station

scholarly journals Application of feed forward and recurrent neural networks in simulation of left ventricular mechanics

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Yaghoub Dabiri ◽  
Alex Van der Velden ◽  
Kevin L. Sack ◽  
Jenny S. Choy ◽  
Julius M. Guccione ◽  
...  
Keyword(s):  
Real Time ◽  
Material Properties ◽  
In Silico ◽  
Prediction Models ◽  
Heart Function ◽  
Active Material ◽  
Experimental Studies ◽  
Left Ventricular ◽  
Feed Forward ◽  
Ventricular Mechanics

AbstractAn understanding of left ventricle (LV) mechanics is fundamental for designing better preventive, diagnostic, and treatment strategies for improved heart function. Because of the costs of clinical and experimental studies to treat and understand heart function, respectively, in-silico models play an important role. Finite element (FE) models, which have been used to create in-silico LV models for different cardiac health and disease conditions, as well as cardiac device design, are time-consuming and require powerful computational resources, which limits their use when real-time results are needed. As an alternative, we sought to use deep learning (DL) for LV in-silico modeling. We used 80 four-chamber heart FE models for feed forward, as well as recurrent neural network (RNN) with long short-term memory (LSTM) models for LV pressure and volume. We used 120 LV-only FE models for training LV stress predictions. The active material properties of the myocardium and time were features for the LV pressure and volume training, and passive material properties and element centroid coordinates were features of the LV stress prediction models. For six test FE models, the DL error for LV volume was 1.599 ± 1.227 ml, and the error for pressure was 1.257 ± 0.488 mmHg; for 20 LV FE test examples, the mean absolute errors were, respectively, 0.179 ± 0.050 for myofiber, 0.049 ± 0.017 for cross-fiber, and 0.039 ± 0.011 kPa for shear stress. After training, the DL runtime was in the order of seconds whereas equivalent FE runtime was in the order of several hours (pressure and volume) or 20 min (stress). We conclude that using DL, LV in-silico simulations can be provided for applications requiring real-time results.

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