• Korean Journal of Agricultural Science
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• v.46 no.1
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• pp.57-65
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• 2019

The purpose of this study was to develop an environment field monitoring system to measure crop growth. The environment field monitoring system consisted of sensors, a data acquisition system, and GPS. The sensors used in the environment field monitoring system consisted of an ambient sensor, a soil sensor, and an intensity sensor. The temperature and humidity of the atmosphere were measured with the ambient sensor. The temperature, humidity, and EC of the soil were measured with the soil sensor. The data acquisition system was developed using the Arduino controller. The field monitoring data were collected before a rainy day, on a rainy day, and after the rainy day. The measured data using the environment field monitoring system were compared with the Daejeon regional meteorological office data. The correlation between the data from the environment field monitoring system and the data from the Daejeon regional meteorological office was analyzed for performance evaluation. The correlation of the temperature and humidity of the atmosphere was analyzed because the Daejeon regional meteorological office only provided data for the temperature and humidity of the atmosphere. The correlation coefficients were 0.86 and 0.90, respectively. The result showed a good correlation between the data from the environment field monitoring system and the data from the Daejeon regional meteorological office. Therefore, the developed system could be applied to monitoring the field environment of agricultural crops.

• Journal of Biosystems Engineering
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• v.40 no.3
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• pp.165-177
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• 2015

Purpose: Accurate monitoring of soil strength is a key technology applicable to various precision agricultural practices. Soil strength has been traditionally measured using a cone penetrometer, which is time-consuming and expensive, making it difficult to obtain the spatial data required for precision agriculture. To improve the current, inefficient method of measuring soil strength, our objective was to develop and evaluate an in-situ system that could measure horizontal soil strength in real-time, while moving across a soil bin. Methods: Multiple cone-shape penetrometers were horizontally assembled at the front of a vertical plow blade at intervals of 5 cm. Each penetrometer was directly connected to a load cell, which measured loads of 0-2.54 kN. In order to process the digital signals from every individual transducer concurrently, a microcontroller was embedded into the measurement system. Wireless data communication was used between a data storage device and this real-time horizontal soil strength (RHSS) measurement system travelling at 0.5 m/s through an indoor experimental soil bin. The horizontal soil strength index (HSSI) measured by the developed system was compared with the cone index (CI) measured by a traditional cone penetrometer. Results: The coefficient of determination between the CI and the HSSI at depths of 5 cm and 10 cm ($r^2=0.67$ and 0.88, respectively) were relatively less than those measured below 20 cm ($r^2{\geq}0.93$). Additionally, the measured HSSIs were typically greater than the CIs for a given numbers of compactor operations. For an all-depth regression, the coefficient of determination was 0.94, with a RMSE of 0.23. Conclusions: A HSSI measurement system was evaluated in comparison with the conventional soil strength measurement system, CI. Further study is needed, in the form of field tests, on this real-time measurement and control system, which would be applied to precision agriculture.

• Journal of the Korean Society of Clothing and Textiles
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• v.40 no.4
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• pp.641-649
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• 2016

Highly precision body measurements represent basic data required by industry and researches who wish to utilize information about the human body. The proficiency and expertise of the measurers have a significant influence on the error and accuracy of data when various parts from multiple subjects' bodies are measured. Therefore, in order to measure accurate body measurements (when measuring bodies directly), it is necessary to conduct objective analyses on errors. This study calculated the Relative Technical Error of Measurement (%TEM) using data that measured each of 24 subjects and discussed errors and methods to reduce errors by conducting comparison analysis based on measured items and objects. The result of analysis indicated that the errors based on age and gender of the objects of measurement were minor; however, there were comparatively distinct differences in measured errors based on measured items. 'Right and left Shoulder Angle' for all measured subjects displayed the greatest errors and standard deviations. 'Height' dimension, Lateral Malleolus Height and Head Height had big errors; in addition, 'Circumference', Neck Base Circumference and Armscye Circumference also had big errors. More careful measurements of such items with big errors require additional educational plan such as a proposal for more objective and detailed measurement methods. Items with small errors but big standard deviations such as Waist Circumference, Calf Circumference, Minimum Leg Circumference, Chest Circumference, Hip Circumference and Waist Circumference confirmed that errors for them greatly decreased with repeated experiments and resultant measurers increased proficiency; consequently, repeated measuring experiments for these items greatly enhance accuracy.

• Journal of Korean Biological Nursing Science
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• v.23 no.4
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• pp.287-297
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• 2021

Purpose: This study aimed to evaluate the correlation and agreement of physical activity (PA) between data obtained from wearable Actigraph devices and self-reporting questionnaires, and to investigate the relationship between psychological state (depression, anxiety, and fatigue) and PA. Methods: A descriptive study was conducted using physical measurements and surveys. PA was measured through both the International Physical Activity Questionnaire (IPAQ) and the Actigraph GT3X+ device. The demographic characteristics of the subjects, as well as their depression, anxiety, and fatigue scores, were collected with structured questionnaires. The Spearman's rank correlation coefficient and the Bland-Altman plot method were employed. Results: Data from 36 healthy adults were analyzed. The overall levels of PA measured using the IPAQ and the Actigraph were 1,891.69 MET min/week and 669.96 MET/day, respectively. Total levels of PA did not show a significant correlation between the two measurement methodologies. However, the moderate-intensity PA resulting from the IPAQ scores showed a significant positive correlation with the light-intensity PA recorded by the Actigraph. The Bland-Altman plot analysis demonstrated that the levels of PA as measured by the two different methods did not match. In addition, PA measured using the Actigraph showed a significant negative correlation with depression and anxiety whereas PA measured using the IPAQ showed a significant positive correlation with fatigue. Conclusion: The conclusion of this study is that the data obtained from the subjective self-reporting questionnaire and the wearable Actigraph do not correlate or match in healthy adults. Future research should investigate the relationship between depression and PA intensity through the Actigraph, or other wearable devices equipped with smartphone apps.

• Journal of Korean Society for Atmospheric Environment
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• v.28 no.6
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• pp.697-705
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• 2012

Particulate matter (PM) data collected from the Urban Air Monitoring Network in Busan during the period from 2006 through 2010 were statistically examined and analyzed to estimate the probability of exceeding $PM_{2.5}$ 24 hour and annual standard to be implemented from January $1^{st}$, 2015. For Jangrimdong, Yeonsandong, Kijangeup, and Jwadong where simultaneous measurement of $PM_{10}$ and $PM_{2.5}$ was conducted, the probability of exceeding $PM_{2.5}$ standards was estimated using $PM_{2.5}$ data measured on site. For other areas where there were no measured $PM_{2.5}$ data available, the probability of exceeding $PM_{2.5}$ standards was statistically estimated using $PM_{10}$ measured on site and $PM_{2.5}/PM_{10}$ ratios obtained from the four stations where both $PM_{2.5}$ and $PM_{10}$ were monitored simultaneously. At Jangrimdong, Yeonsandong, Kijangeup, and Jwadong, mean value of annual 99 percentile of 24 hr average $PM_{2.5}$ for 5 years from 2006 through 2010 was 99.3, 74.5. 57.0, and $62.5{\mu}g/m^3$, respectively, and the probability of exceeding $PM_{2.5}$ 24 hr standard was estimated at 100%. For areas where there were no measured $PM_{2.5}$ data available, the estimated probability of exceeding $PM_{2.5}$ 24 hr standard was more than 0.82. Mean value of annual average $PM_{2.5}$ from 2008 through 2010 was 31.7 and $27.6{\mu}g/m^3$ for Jangrimdong and Yeonsandong, respectively, which exceeded $PM_{2.5}$ annual standard of $25{\mu}g/m^3$. Mean value of annual average $PM_{2.5}$ during the same period for Kijangeup and Jwadong was 19.2 and $20.7{\mu}g/m^3$, respectively, which satisfied $PM_{2.5}$ annual standard. For other areas where there were no measured $PM_{2.5}$ data available, the probability of exceeding $PM_{2.5}$ annual standard was more than 0.95 except Taejongdae and Kwangahndong. With $PM_{10}$ and $PM_{2.5}$ data measured at 17 Urban Air Monitoring Stations in Busan, the probability of exceeding $PM_{2.5}$ standards was estimated to be very high for almost all areas. This result indicates that proper measures to mitigate $PM_{2.5}$ in Busan should be investigated and established as soon as possible.

• Progress in Medical Physics
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• v.33 no.4
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• pp.150-157
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• 2022

Purpose: Elekta synergy^® was commissioned in the Seoul National University Veterinary Medical Teaching Hospital. Recently, Chung-Ang University Gwang Myeong Hospital commissioned Elekta Versa HD^TM. The beam characteristics of both machines are similar because of the same Agility^TM MLC Model. We compared measured beam data calculated using the Elekta treatment planning system, Monaco^®, for each institute. Methods: Beam of the commissioning Elekta linear accelerator were measured in two independent institutes. After installing the beam model based on the measured beam data into the Monaco^®, Monte Carlo (MC) simulation data were generated, mimicking the beam data in a virtual water phantom. Measured beam data were compared with the calculated data, and their similarity was quantitatively evaluated by the gamma analysis. Results: We compared the percent depth dose (PDD) and off-axis profiles of 6 MV photon and 6 MeV electron beams with MC calculation. With a 3%/3 mm gamma criterion, the photon PDD and profiles showed 100% gamma passing rates except for one inplane profile at 10 cm depth from VMTH. Gamma analysis of the measured photon beam off-axis profiles between the two institutes showed 100% agreement. The electron beams also indicated 100% agreement in PDD distributions. However, the gamma passing rates of the off-axis profiles were 91%-100% with a 3%/3 mm gamma criterion. Conclusions: The beam and their comparison with MC calculation for each institute showed good performance. Although the measuring tools were orthogonal, no significant difference was found.

• Korean Journal of Environmental Agriculture
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• v.33 no.1
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• pp.37-43
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• 2014

BACKGROUND: Water quality data are collected less frequently than flow data because of the cost to collect and analyze, while water quality data corresponding to flow data are required to compute pollutant loads or to calibrate other hydrology models. Regression models are applicable to interpolate water quality data corresponding to flow data. METHODS AND RESULTS: A regression model was suggested which is capable to consider flow and time variance, and the regression model coefficients were calibrated using various measured water quality data with genetic-algorithm. Both LOADEST and the regression using genetic-algorithm were evaluated by 19 water quality data sets through calibration and validation. The regression model using genetic-algorithm displayed the similar model behaviors to LOADEST. The load estimates by both LOADEST and the regression model using genetic-algorithm indicated that use of a large proportion of water quality data does not necessarily lead to the load estimates with smaller error to measured load. CONCLUSION: Regression models need to be calibrated and validated before they are used to interpolate pollutant loads, as separating water quality data into two data sets for calibration and validation.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.4
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• pp.1401-1411
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• 2013

The inflow from Daejeo gate and the outflow from Noksan gate in Seonakdong river, which is a reservoir-like river with the characteristics of stagnant water, is very important for TMDLs (Total Maximum Daily Load) of Nakbon N unit watershed. However, there are very few measured data for calculating base flow. The operating data is much different from the actual measured data because the probable maximum flowrate is used for gate operation. In this study, the operating flowrate data of Daejeo and Noksan gates are compared with the monthly water quality data from Ministry of Environment monitoring station. The dynamic change of the flowrate and the water quality is also measured for the operation of Daejeo and Noksan gates. The flowrate of Seonakdong river is measured, which is affected by the gate operation, the water elevation difference of Nakdong river and Seonakdong river, and the tidal elevation. The results of this study can be used for establishing the water quality improvement measures by the optimal gate operation.

• Journal of the Korea Safety Management & Science
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• v.17 no.2
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• pp.129-137
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• 2015

This study is in regard to the gas detection system and gas detection method utilizing smart phone. This study includes; 1) the sensor module attached to the smart phone to detect and measure flammable gas or toxic gas; and 2) gas detection APP which is installed inside the smart phone and recognizes the user information and location information automatically by reading RFID tag indicating the user or the location to detect gas through the contact area where RFID and blue tooth reader is installed inside of the above mentioned smart phone, and then measures the combustible gas or toxic gas by operating above mentioned sensor module and obtains the data thus measured, and above mentioned smart phone is characterized by its transmission of the above mentioned user information, location information and measured data which are obtained by above mentioned gas detecting APP to operation server via communication network. With this, reliability for the location detecting gas by the user, the result of the measurement, etc. can be secured. Furthermore, this provides the effect of preventing artificial manipulation at the time of input which is associated with the identification of the user to be measured by utilizing removable sensor module and application or the mistake resulted from wrong input by the user. In addition, by transmitting the measured data from the sensor module carrying out gas detection to operation server, this provides the effect of making it possible to process the data thus collected to a specialized data for combustible gas or toxic gas.

• Ocean and Polar Research
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• v.44 no.4
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• pp.287-296
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• 2022

High-frequency (HF) radar measures sea surface currents from the radio waves transmitted and received by antenna on land. Since the data quality of HF radar measurements sensitively depend on the radio wave environment around antenna, Antenna Pattern Measurements (APM) plays an important role in evaluating the accuracy of measured surface currents. In this study, APM was performed by selecting the times when the background noise level around antenna was high and low, and radial data were generated by applying the ideal pattern and measured pattern. The measured antenna pattern for each case was verified with the current velocity data collected by drifters. The radial velocity to which the ideal pattern was applied was not affected by the background noise level around antenna. However, the radial velocity obtained with APM in the period of high background noise was significantly lower in quality than the radial velocity in a low noise environment. It is recomended that APM be carried out in consideration of the radio wave environment around antenna, and that the applied result be compared and verified with the current velocity measurements by drifters. If it is difficult to re-measure APM, we suggest using radial velocity in generating total vector with the ideal pattern through comparative verification, rather than poorly measured patterns, for better data quality.