scholarly journals Manufacture and Sensor’s Characterization for Optimizing the Manufacture of Resistance Sensors Based on Honeycomb and Honey Market

2020 ◽  
Vol 3 ◽  
pp. 259-261
Author(s):  
Ayu Sakila Arum ◽  
Dyah Imas Cholidina ◽  
Frida Agung Rakhmadi
Keyword(s):  
Transfer Function ◽  
Correlation Coefficient ◽  
Linear Correlation ◽  
Power Supply ◽  
Copper Wire ◽  
Sensor Design ◽  
Volume Variation ◽  
Two Samples ◽  
Three Stages

This research aims to identify resistance sensors based on honeycomb and honey market. It was conducted in three stages, namely sensor design, sensor manufacturing and sensor characterization. The sensor scheme has been made by software fritzing. The tools used in the sensor manufacture are PCB sensors, resistors and copper wire. The tools used in sensor characterization are provided by power supply, CRO and samples. Sensor testing is done by varied the sample into 6 variations accurately 5ml, 10ml, 15ml, 20ml, 25ml, and 30ml. Data generated from the sensor in the form of voltage. Repetition on each volume variation was 10 times. Then, It was be processed by calculating the compatibility and the average of the voltage produced along with its uncertainty. The results showed the average of the voltage generated with the uncertainty at 5ml volume (9,052 ± 0,001) V and (8,682 ± 0.017) V, for volume of 10ml (9,040 ± 0.025) V and (8,531 ± 0.050) V, for volume of 15ml (8,947 ± 0.073) V and (8.230 ± 0.018) V, for volume of 20ml (8.999 ± 0.073) V and (8,209 ± 0.007) V, for volume of 25ml (8.967 ± 0.080) V and (7.689 ± 0.048)V and for volume of 30ml (8,925 ± 0,040) V and (8,284 ± 0,018) V. Repeatibility has produced on this sensor was obtained from two samples, honeycomb by 90.919% and honey market by 94.432%. Based on the results obtained from the calculation, it can be determined that the transfer function was V = 9.06853x-0.0045828571 and V = 8.72453x-0.0259257143. The linear correlation coefficient obtained by 95.9%. This research will be used for optimizing the manufacture of resistance sensors with good characteristics.

scholarly journals Design and Characterization of Resistance Sensor to Optimize the Manufacturing of Infuse Water Lemon and Sprite Water Lymon Detection System

2020 ◽  
Vol 3 ◽  
pp. 9-11
Author(s):  
Fia Ismi Nur Alfiah ◽  
Dinda Salsabila P ◽  
Frida Agung Rakhmadi
Keyword(s):  
Transfer Function ◽  
Data Acquisition ◽  
Power Supply ◽  
Detection System ◽  
Copper Wire ◽  
Sensor Design ◽  
Detection Tool ◽  
Three Phases

This research was purposed to design and characterize a resistance sensor. This research was conducted with three phases: designing, manufacturing and characterization of resistance sensor. Sensor design was done using the 3D paint software. The tools used in sensor manufacturing were PCB, resistors, and copper wire. The tools used in sensor characterization were power supply, ohmmeter and samples of infuse water lemon and sprite water lymon. Sensor characterization was done by varying samples of 50ml, 100ml, 150ml. Data acquisition for each of samples was repeated 3 times. The result of this research was showed an average and unpredictability with the repeatability resistance sensor on the sample of infuse water lemon (1,215 ± 0,723) V with repeatability 99.05% while for samples of sprite water lymon amounting to (1,12 ± 1,345) V with repeatability 99.24%. And was retrieved the value of the transfer function for sample infuse water lemon is y = (4,757-0.03542x) and for sample sprite water lymon is y = (1.40666-0.0028666x). This tool can be developed as a detection tool using resistance-based sensors.

scholarly journals Manufacture and Characterization of Sensor for Optimizing the Manufacture of Resistance Sensor for Pure Orange Drink and Unpure Orange Drink

2020 ◽  
Vol 3 ◽  
pp. 159-161
Author(s):  
Siti Annisa Widyaningrum ◽  
Ratri Ismawati ◽  
Frida Agung Rakhmadi
Keyword(s):  
Quality Control ◽  
Control System ◽  
Power Supply ◽  
Copper Wire ◽  
Quality Control System ◽  
High Grade ◽  
Sensor Design ◽  
The Future ◽  
Data Result

This research was purposed to design and analyze a resistance sensor. This research was done by three steps which are designing, manufacturing and characterizing of resistance sensor. Sensor design was done by using Fritzing software. The tools we used in manufacturing sensor were PCB, resistor, and copper wire. The tools we used in sensor characterizing were power supply, CRO and samples of pure orange drink and unpure orange drink. Sensor characterizing was done by varying samples of 50 ml and 100 ml. Data acquisitiom for each samples were repeated five times. Data generated from the sensor in the form of voltage. The data result of measurement were calculated by its average and its uncertainty. For pure orange drink 50 mL (2.274 ± 1.137); 100 mL (1.688 ± 0.844) and for unpure orange drink 50 mL (2.582 ± 1.291); 100 mL (1.852 ± 0.926). Therefore in the future we can make quality control system of orange drink by optimizing the manufacture of resistance sensors with high-grade characteristics.

scholarly journals Power distribution system fault monitoring device for supply networks in Nigeria

2019 ◽  
Vol 9 (4) ◽  
pp. 2803
Author(s):  
Olalekan Kabiru Kareem ◽  
Aderibigbe Adekitan ◽  
Ayokunle Awelewa
Keyword(s):  
Power Supply ◽  
Power Distribution ◽  
Distribution System ◽  
Low Voltage ◽  
Copper Wire ◽  
Low Cost ◽  
Monitoring Device ◽  
Power Distribution System ◽  
Fault Monitoring ◽  
Quick Service

Electric power is the bedrock of our modern way of life. In Nigeria, power supply availability, sufficiency and reliability are major operational challenges. At the generation and transmission level, effort is made to ensure status monitoring and fault detection on the power network, but at the distribution level, particularly within domestic consumer communities there are no fault monitoring and detection devices except for HRC fuses at the feeder pillar. Unfortunately, these fuses are sometimes replaced by a copper wire bridge at some locations rendering the system unprotected and creating a great potential for transformer destruction on overload. This study is focused on designing an on-site power system monitoring device to be deployed on selected household entry power cables for detecting and indicating when phase off, low voltage, high voltage, over current, and blown fuse occurs on the building’s incomer line. The fault indication will help in reducing troubleshooting time and also ensure quick service restoration. After design implementation, the test result confirms design accuracy, device functionality and suitability as a low-cost solution to power supply system fault monitoring within local communities.

A selection of remarks on the measurement of correlations between variables of a panel data structure

2021 ◽  
Vol 66 (5) ◽  
pp. 7-25
Author(s):  
Mieczysław Kowerski ◽  
Jarosław Bielak
Keyword(s):  
Panel Data ◽  
Correlation Coefficient ◽  
Linear Correlation ◽  
Stock Exchange ◽  
Panel Data Analysis ◽  
Correlation Coefficients ◽  
Data Dependencies ◽  
Construction Companies ◽  
Advantages And Disadvantages ◽  
Warsaw Stock Exchange

Many articles featuring panel data modelling tend to begin their considerations with an introduction of the Pearson linear correlation coefficients matrix between the analysed variables. The aim of the article is to prove such an approach unsuitable in the analysis of panel data dependencies. Instead, an attempt has been made to propose a more appropriate measure – a correlation coefficient between the empirical and fitted values of the dependent variable of the estimated panel model (with fixed or random effects) in relation to the variable whose dependency towards the dependent variable is being studied. Pearson’s linear correlation coefficient does not reflect the basic advantage of panel data, which is the ability to provide information about the dependencies of the studied phenomena simultaneously in time and space. The fact that one observation relates to object i during period t and another to object j during period t + 1 is irrelevant for the calculation of the coefficient. Pearson’s coefficient, however, can be used when conducting sub-calculations in panel data analysis. The presented considerations have been illustrated by the calculations of the relationships between the structure of capital and the profitability and size of 17 construction companies listed on the Warsaw Stock Exchange in the years 2009–2018 (170 observations) which created a balanced panel. A specification of the advantages and disadvantages of the proposed solution was formulated on the basis of the calculations.

Serious Analytical and Conceptual Errors in Bilirubin Binding Studies

PEDIATRICS ◽  
1981 ◽  
Vol 67 (6) ◽  
pp. 935-936
Author(s):  
Charles E. Ahlfors
Keyword(s):  
Correlation Coefficient ◽  
Linear Correlation ◽  
Accurate Method ◽  
Binding Studies ◽  
Low Correlation ◽  
Total Capacity ◽  
Bilirubin Binding

The unexpectedly high and 1:1 correlation among fluorometric, Sephadex, and peroxidase determinations of total and reserve bilirubin binding capacities as reported by Cashore et al1 is quite remarkable considering the serious analytical and conceptual errors in the article, the previously observed lack of linear correlation between the fluorometer and 2-(4'-hydroxybenzene)azobenzoic acid (HABA),2 and the relatively low correlation coefficient (.92) observed when the peroxidase test is compared with itself on different instruments.3 The addition of sufficient bilirubin to near saturation of the first site should make the fluorometer a reasonably accurate method of assessing "true" total capacity (usually thought to be 1 mole of bilirubin bound per mole of albumin).

scholarly journals Operational Risk Aggregation Based on Business Line Dependence: A Mutual Information Approach

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Wenzhou Wang ◽  
Limeng Shi ◽  
Xiaoqian Zhu
Keyword(s):  
Mutual Information ◽  
Correlation Coefficient ◽  
Linear Correlation ◽  
Risk Estimation ◽  
Pearson Correlation ◽  
Operational Risk ◽  
Random Variable ◽  
Data Set ◽  
Risk Aggregation ◽  
Business Line

The dependencies between different business lines of banks have serious effects on the accuracy of operational risk estimation. Furthermore, the dependencies are far more complicated than simple linear correlation. While Pearson correlation coefficient is constructed based on the hypothesis of a linear association, the mutual information that measures all the information of a random variable contained in another random variable is a powerful alternative. Based on mutual information, the generalized correlation coefficient which can capture both linear and nonlinear correlation can be derived. This paper models the correlation between business lines by mutual information and normal copula. The experiment on a real-world Chinese bank operational risk data set shows that using mutual information to model the dependencies between business lines is more reasonable than linear correlation.

scholarly journals Correlation of plasma concentrations of cystatin C and creatinine to inulin clearance in a pediatric population

1998 ◽  
Vol 44 (6) ◽  
pp. 1334-1338 ◽  
Author(s):  
Douglas Stickle ◽  
Barbara Cole ◽  
Karl Hock ◽  
Keith A Hruska ◽  
Mitchell G Scott
Keyword(s):  
Correlation Coefficient ◽  
Cystatin C ◽  
Linear Correlation ◽  
Pediatric Patients ◽  
Plasma Concentrations ◽  
Age Groups ◽  
Inulin Clearance ◽  
Blood Concentrations ◽  
Group A ◽  
Group B

Abstract Measurement of blood concentrations of cystatin C (cysC), a cysteine protease inhibitor present in human plasma, has been suggested for use as an indicator of glomerular filtration rate (GFR) in a manner analogous to the use of plasma creatinine (SCR). In this study, cysC and SCR were measured in plasma from pediatric patients (4–19 years) with renal disease for whom a “gold standard” measurement of GFR via inulin clearance (CIN) was available. The data analyses were divided into two age groups: group A (4–12 years, n = 26) and group B (12–19 years, n = 34). For both age groups, the linear correlation coefficient of [cysC]−1 vs CIN (mL/min/1.73 m2) (r = 0.765 for group A and r = 0.869 for group B) was less than that of the linear correlation coefficient of [SCR]−1 vs CIN (r = 0.841 for group A and r = 0.892 for group B). As a single measurement for detection of abnormal GFR, however, the optimum receiver-operator characteristic point for cysC measurement (for group A at cysC >1.2 mg/L, sensitivity = 80%, specificity = 91%; and for group B at cysC >1.4 mg/L, sensitivity = 87%, specificity = 100%) was numerically superior to that for SCR measurement (for group A at SCR >8.0 mg/L, sensitivity = 67%, specificity = 100%; and for group B at SCR >9.0 mg/L, sensitivity = 91%, specificity = 91%), using a reference value for normal GFR of CIN > 90 mL/min/1.73 m2. However, these differences were not statistically significant. CysC measurement appears to be broadly equivalent to SCR measurement for estimation of GFR in pediatric patients.

scholarly journals Comparison of Emission Measurement Methods for Electromagnetic Disturbances in the Frequency Range from 30 MHz to 300 MHz for LED Lamps According to EN 55015

2019 ◽  
Vol 63 (2) ◽  
pp. 106-111
Author(s):  
Kazimierz Kamuda ◽  
Dariusz Klepacki ◽  
Kazimierz Kuryło ◽  
Wiesław Sabat
Keyword(s):  
Correlation Coefficient ◽  
Linear Correlation ◽  
Traditional Method ◽  
Measurement Methods ◽  
The Other ◽  
Emission Measurement ◽  
Frequency Range ◽  
Statistical Tools ◽  
Equivalent Method ◽  
Electromagnetic Disturbances

The results of measurements of electromagnetic disturbances emitted by LED lamps, in the frequency range from 30 MHz to 300 MHz, which were made using two methods described in the EN 55015/2013 standard have been presented in the paper. In order to compare both methods, each tested lamp was first measured using the traditional method described in Section4.4.2 and then tested by an alternative, equivalent method described in Annex B of the above-mentioned standard. The comparison of results for both methods indicates that using first method, a given LED lamp emits disturbances below the acceptable limits, while the same LED lamp tested with the second method emits disturbances that are at the limit of admissible values. Additionally, used statistical tools in the form of calculated linear correlation coefficient show that the nature of the emission of disturbances measured for the same lamp is very comparable in both methods. The reference of these quasi-peak values to the permissible limits applicable in one or the other method may lead to different decisions.

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