scholarly journals Monitoring Infusion Pump Via Wireless (Occlusion part)

2020 ◽  
Vol 2 (1) ◽  
pp. 34-41
Author(s):  
Nisa'ul Sholihah ◽  
Abd Kholiq ◽  
Sumber Sumber
Keyword(s):  
Data Processing ◽  
Flow Rate ◽  
Medical Equipment ◽  
Infusion Pump ◽  
Minute Volume ◽  
Stepper Motor ◽  
Intravenous Fluids ◽  
Rate Data ◽  
Motor Controller ◽  
Is Design

Infuse pump is a medical equipment which is design to control and regulate the administration of intravenous fluids in the treatment.. This module uses the L298N motor driver as a stepper motor controller. The choice of the infuse pump setting is the volume setting from 100 ml to 500 ml and the speed setting of 30 ml / hour, 60 ml / hour, and 90 ml / hour. The author uses the Atmega 328 microcontroller as a droplet controller per minute, volume and speed. Occlusion in this device is in the detector of the droplets that are alerted in the presence of a sound buzzer. This tool is also equipped with monitoring volume, tpm and speed on a wireless-based PC using HC-11 as a transmission from module to PC. This tool is equipped with oclusion. The flow rate data processing in IDA from infusion got the highest error result at the setting of 30 ml / hour which was equal to 5.97%. the highest error for the calculation of droplets in the module is the setting of 30 ml / hour which is equal to 32% and manually at setting 60 which is 23%.

scholarly journals Method to complete flow rate data in automatic fluviometric stations in the karst system of Lagoa Santa area, MG, Brazil

2020 ◽  
Vol 50 (4) ◽  
Author(s):  
Rodrigo Sérgio de Paula ◽  
Leila Nunes Menegasse Velásquez
Keyword(s):  
Flow Rate ◽  
Rate Data ◽  
Karst System

scholarly journals Artificial Neural Network for Daily Low Stream Flow Rate Prediction of Perigiali Stream, Kavala City, NE Greece

Proceedings ◽  
2018 ◽  
Vol 2 (11) ◽  
pp. 578
Author(s):  
Thomas Papalaskaris ◽  
Theologos Panagiotidis
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Flow Rate ◽  
Stream Flow ◽  
Low Flow ◽  
Urban Stream ◽  
Neural Network Modeling ◽  
Rate Data ◽  
Artificial Neural

Only a few scientific research studies with reference to extremely low stream flow conditions, have been conducted in Greece, so far. Forecasting future low stream flow rate values is a crucial and desicive task when conducting drought and watershed management plans, designing water reservoirs and general hydraulic works capacity, calculating hydrological and drought low flow indices, separating groundwater base flow and storm flow of storm hydrographs etc. Artificial Neural Network modeling simulation method generates artificial time series of simulated values of a random (hydrological in this specific case) variable. The present study produces artificial low stream flow time series of both a part of the past year (2016) as well as the present year (2017) considering the stream flow data observed during two different respecting interval period of the years 2016 and 2017. We compiled an Artificial Neural Network to simulate low stream flow rate data, acquired at a certain location of the partly regulated semi-urban stream which runs through the eastern exit of Kavala city, NE Greece, using a 3-inches U.S.G.S. modified portable Parshall flume, a 3-inches conventional portable Parshall flume, a 3-inches portable Montana (short Parshall) flume and a 90° V-notched triangular shaped sharp crested portable weir plate. The observed data were plotted against the predicted one and the results were demonstrated through interactive tables providing us the ability to effectively evaluate the ANN model simulation procedure performance. Finally, we plot the recorded against the simulated low stream flow rate data, compiling a log-log scale chart which provides a better visualization of the discrepancy ratio statistical performance metrics and calculate the derived model statistics featuring the comparison between the recorded and the forecasted low stream flow rate data.

scholarly journals Forecasting Low Stream Flow Rate Using Monte—Carlo Simulation of Perigiali Stream, Kavala City, NE Greece

Proceedings ◽  
2018 ◽  
Vol 2 (11) ◽  
pp. 580 ◽  
Author(s):  
Thomas Papalaskaris ◽  
Theologos Panagiotidis
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Flow Rate ◽  
Stream Flow ◽  
Simulation Method ◽  
Base Flow ◽  
Low Flow ◽  
Rate Data ◽  
Storm Flow ◽  
Management Plans

A small number of scientific research studies with reference to extremely low flow conditions, have been conducted in Greece, so far. Predicting future low stream flow rate values is an essential and of paramount importance task when compiling watershed and drought management plans, designing water reservoirs and general hydraulic works capacity, calculating hydrological and drought low flow values, separating groundwater base flow and storm flow of storm hydrographs etc. The Monte-Carlo simulation method generates multiple attempts to define the anticipated value of a random (hydrological in this specific case) variable. The present study compiles, correspondingly, artificial low stream flow time series of both the same part of the year (2016) as well as a part of the calendar year (2017), based on the stream flow data observed during the same two different interval periods of the years 2016 and 2017, using a 3-inches U.S.G.S. modified portable Parshall flume, a 3-inches conventional portable Parshall flume, a 3-inches portable Montana (short Parshall) flume and a 90° V-notched triangular shaped sharp crested portable weir plate. The recorded data were plotted against the fitted one and the results were demonstrated through interactive tables providing us the ability to effectively evaluate the simulation procedure performance. Finally, we plot the observed against the calculated low stream flow rate data, compiling a log-log scale chart which provides a better visualization of the discrepancy ratio statistical performance metric and calculate statistics featuring the comparison between the recorded and the forecasted low stream flow rate data.

Simultaneous Interpretation of Pressure, Temperature, and Flow-Rate Data Using Bayesian Inversion Methods

2010 ◽  
Vol 14 (02) ◽  
pp. 225-238 ◽  
Author(s):  
Obinna O. Duru ◽  
Roland N. Horne
Keyword(s):  
Flow Rate ◽  
Bayesian Methods ◽  
Temperature Measurements ◽  
Bayesian Inversion ◽  
Rate Data ◽  
Simultaneous Interpretation ◽  
Data Limitations ◽  
Regularization Parameters ◽  
Deconvolution Problem ◽  
Downhole Measurements

Summary Current downhole measuring technologies have provided a means of acquiring downhole measurements of pressure, temperature, and sometimes flow-rate data. Jointly interpreting all three measurements provides a way to overcome data limitations that are associated with interpreting only two measurements—pressure and flow-rate data—as is currently done in pressure-transient analysis. This work shows how temperature measurements can be used to improve estimations in situations where lack of sufficient pressure or flow-rate data makes parameter estimation difficult or impossible. The model that describes the temperature distribution in the reservoir lends itself to quasilinear approximations. This makes the model a candidate for Bayesian inversion. The model that describes the pressure distribution for a multirate flow system is also linear and a candidate for Bayesian inversion. These two conditions were exploited in this work to present a way to cointerpret pressure and temperature signals from a reservoir. Specifically, the Bayesian methods were applied to the deconvolution of both pressure and temperature measurements. The deconvolution of the temperature measurements yielded a vector that is linearly related to the average flow-rate from the reservoir and, hence, could be used for flow-rate estimation, especially in situations in which flow-rate measurements are unavailable or unreliable. This flow rate was shown to be sufficient for a first estimation of the pressure kernel in the pressure-deconvolution problem. When the appropriate regularization parameters are chosen, the Bayesian methods can be used to suppress fluctuations and noise in measurements while maintaining sufficient resolution of the estimates. This is the point of the application of the method to data denoising. In addition, because Bayesian statistics represent a state of knowledge, it is easier to incorporate certain information, such as breakpoints, that may help improve the structure of the estimates. The methods also lend themselves to formulations that make possible the estimation of initial properties, such as initial pressures.

Model-Based Evaluation of Eustachian Tube Mechanical Properties Using Continuous Pressure–Flow Rate Data

2002 ◽  
Vol 30 (8) ◽  
pp. 1064-1076 ◽  
Author(s):  
Samir N. Ghadiali ◽  
J. Douglas Swarts ◽  
William J. Federspiel
Keyword(s):  
Mechanical Properties ◽  
Eustachian Tube ◽  
Flow Rate ◽  
Rate Data ◽  
Pressure Flow ◽  
Model Based
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