• Journal of Biosystems Engineering
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• v.3 no.2
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• pp.114-125
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• 1978

The use of petroleum fuels in grain drying causes problems of high cost and management. To solve these problems, it is required to study on soLar energy as an alternative to petroleum fuels for grain drying. The purposes of this study were to find out the optimum received area and air flow rate of a flat-plate solar air collector for grain drying and to assess its effects on grain drying with a small grain bin. The results of this study are summarized as follows ; 1. The calculated optimum tilt angles of the collector in the summer and autumn drying seasons were 20 and 50 degress, respectively, in suwon area. 2. The outlet temperature of the collector was $36^\circ C$ on the daily average with the maximum of $36^\circ C$ at 12:00 o clock. Solar radiation on the collector surface was 1.04 ly( 1 langley = 1 cal/$cm^2$) per minute on the daily average and 1.30 ly per minute on the maximum at 11:00am. The thermal efficiency of the collector was 62.4 percent on the daily average, and the air flow-rate per unit receiving are was 1.03 $m^3$/min/$m^2$.4. The calculated optimum receiving area and the air flow-rate per unit cubic volume for paddy in autumn drying season was 2 $m^2$ and 2$m^3$/min , respectively. 5. not significantly difference in the collector efficiency was appeared between the rotating and fixed type of solar collector. 6. For drying of wheat with 0.6 meter of the depth in the bin, approximately 9 hours were required to reduce the moisture content from 21.6% to 13% with air follow rate of 5 $m^3$/min an initial moisture per cubic meter of wheat and with air temperature of $52^\circ C$. 7. In the drying test of rough rice with a turning operation in a grain bin approximately 21 hours were required to reduced the moisture from 21% to 14.5% with airflow rate of 2 $m^3$/min per cubic meter of rice and the air temperature of $43.5^\circ C$. 8. Over-drying at the bottom and less -drying at the top of the grain mass was resulted from the high -temperature of drying air which was obtained from the flat-plate solar collector in this test. An appropriate operation should be prepared for the uniform moisture of the grain in the bin.

• International Journal of Highway Engineering
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• v.19 no.6
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• pp.83-95
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• 2017

PURPOSES : The purpose of this study is to develop a regression model to predict the International Roughness Index(IRI) and Surface Distress(SD) for the estimation of HPCI using Expressway Pavement Management System(PMS). METHODS : To develop an HPCI prediction model, prediction models of IRI and SD were developed in advance. The independent variables considered in the models were pavement age, Annual Average Daily Traffic Volume(AADT), the amount of deicing salt used, the severity of Alkali Silica Reaction(ASR), average temperature, annual temperature difference, number of days of precipitation, number of days of snowfall, number of days below zero temperature, and so on. RESULTS : The present IRI, age, AADT, annual temperature differential, number of days of precipitation and ASR severity were chosen as independent variables for the IRI prediction model. In addition, the present IRI, present SD, amount of deicing chemical used, and annual temperature differential were chosen as independent variables for the SD prediction model. CONCLUSIONS : The models for predicting IRI and SD were developed. The predicted HPCI can be calculated from the HPCI equation using the predicted IRI and SD.

• Journal of The Korean Society of Emergency Medicine
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• v.29 no.6
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• pp.603-615
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• 2018

Objective: We analyzed the association between regional weather and temporal changes on the daily occurrence of trauma emergencies and their severity. Methods: In this cross-sectional prospective study, we investigated daily atmospheric patterns in trauma episodes in 1,344 patients in Cheongju city, South Korea, from January 2016 to December 2016 and analyzed the association of trauma occurrence and Injury Severity Scores (ISS) with weather conditions on a daily scale. Results: The mean age of trauma patients was $53.0{\pm}23.8years$ and average ISS was $9.0{\pm}2.0$. Incidence of trauma was positively correlated with average temperature (r=0.512, P<0.001) and atmospheric pressure (r=0.332, P=0.010) and negatively correlated with air pollutants (particulate matter less than $2.5{\mu}m^3$ [PM2.5], r=-0.629, P<0.001; particulate matter less than $10{\mu}m^3$ [PM10], r=-0.679, P<0.001). ISS was not significantly correlated with climate parameters and air pollutants, and variability was observed in the frequency and severity of trauma by time of day (highest occurrence, 16-20 pm; highest ISS, 4-8 am), day of the week (highest occurrence and highest ISS, Saturday), month of the year (highest occurrence, July; highest ISS, November), and season (highest incidence, summer; highest ISS, autumn). Conclusion: The study shows a positive relationship between trauma occurrence and specific weather conditions, such as atmospheric temperature and pressure. There was a negative relationship between concentrations of PM2.5 or PM10, and trauma occurrence. However, no correlation was observed between weather conditions or the concentrations of air pollutants and ISS. In addition, seasonal, circaseptan, and circadian variations exist in trauma occurrence and severity. Thus, we suggest that evaluation of a larger, population-based data set is needed to further investigate and confirm these relationships.

• The Plant Pathology Journal
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• v.31 no.4
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• pp.350-362
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• 2015

A disease forecast model for bacterial grain rot (BGR) of rice, which is caused by Burkholderia glumae, was developed in this study. The model, which was named 'BGRcast', determined daily conduciveness of weather conditions to epidemic development of BGR and forecasted risk of BGR development. All data that were used to develop and validate the BGRcast model were collected from field observations on disease incidence at Naju, Korea during 1998-2004 and 2010. In this study, we have proposed the environmental conduciveness as a measure of conduciveness of weather conditions for population growth of B. glumae and panicle infection in the field. The BGRcast calculated daily environmental conduciveness, $C_i$, based on daily minimum temperature and daily average relative humidity. With regard to the developmental stages of rice plants, the epidemic development of BGR was divided into three phases, i.e., lag, inoculum build-up and infection phases. Daily average of $C_i$ was calculated for the inoculum build-up phase ($C_{inf}$) and the infection phase ($C_{inc}$). The $C_{inc}$ and $C_{inf}$ were considered environmental conduciveness for the periods of inoculum build-up in association with rice plants and panicle infection during the heading stage, respectively. The BGRcast model was able to forecast actual occurrence of BGR at the probability of 71.4% and its false alarm ratio was 47.6%. With the thresholds of $C_{inc}=0.3$ and $C_{inf}=0.5$, the model was able to provide advisories that could be used to make decisions on whether to spray bactericide at the preand post-heading stage.

• Korean Journal of Agricultural Science
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• v.49 no.2
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• pp.193-202
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• 2022

The recurrent neural network (RNN) algorithm has been widely used in water-related research areas, such as water level predictions and water quality predictions, due to its excellent time series learning capabilities. However, studies on water quality predictions using RNN algorithms are limited because of the scarcity of water quality data. Therefore, most previous studies related to water quality predictions were based on monthly predictions. In this study, the quality of the water in a reservoir in Nonsan, Chungcheongnam-do Republic of Korea was predicted using the RNN-LSTM algorithm. The study was conducted after constructing data that could then be, linearly interpolated as daily data. In this study, we attempt to predict the water quality on the 7th, 15th, 30th, 45th and 60th days instead of making daily predictions of water quality factors. For daily predictions, linear interpolated daily water quality data and daily weather data (rainfall, average temperature, and average wind speed) were used. The results of predicting water quality concentrations (chemical oxygen demand [COD], dissolved oxygen [DO], suspended solid [SS], total nitrogen [T-N], total phosphorus [TP]) through the LSTM algorithm indicated that the predictive value was high on the 7th and 15th days. In the 30th day predictions, the COD and DO items showed R² that exceeded 0.6 at all points, whereas the SS, T-N, and T-P items showed differences depending on the factor being assessed. In the 45th day predictions, it was found that the accuracy of all water quality predictions except for the DO item was sharply lowered.

• Journal of Korean Society of Water and Wastewater
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• v.14 no.3
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• pp.281-290
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• 2000

This study investigates the prediction of daily water supply, which is a necessary for the efficient management of water distribution system. ANFIS, namely artificial intelligence, is a neural network into which fuzzy information is inputted and then processed. In this study, daily water supply was predicted through an application of network-based fuzzy inference system(ANFIS) for daily water supply prediction. This study was investigated methods for predicting water supply based on data about the amount of water which supplied in Kwangju city. For variables choice, four analyses of input data were conducted: correlation analysis, autocorrelation analysis, partial autocorrelation analysis, and cross-correlation analysis. Input variables were (a) the amount of water supply, (b) the mean temperature, and (c) the population of the area supplied with water. Variables were combined in an integrated model. Data of the amount of daily water supply only was modelled and its validity was verified in the case that the meteorological office of weather forecast is not always reliable. Proposed models include accidental cases such as a suspension of water supply. The maximum error rate between the estimation of the model and the actual measurement was 18.46% and the average error was lower than 2.36%. The model is expected to be a real-time estimation of the operational control of water works and water/drain pipes.

• Particle and aerosol research
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• v.19 no.1
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• pp.13-20
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• 2023

The Asian dust observation network operates β-ray attenuation samplers to measure PM₁₀ concentrations. In addition, equivalence evaluation and accuracy inspection(Precision Tests) are conducted every year for the reliability of data. β-ray attenuation samplers(16 units) were comparatively observed from May to June 2020 and from July to December 2021. During the observation period, the average daily temperature was the lowest at 6.4℃ in December and the highest at 27.3℃ in August. The average daily humidity ranged from 60% to 100%, but the average daily humidity was over 75% from July to September. The minimum value of the PM₁₀ Gravimetric method was 5.0 ㎍/m³, the maximum value was 53.4 ㎍/m³, and the average value was 17.8 ㎍/m³. The equivalence evaluation results of the PM₁₀ Gravimetric method and β-ray attenuation samplers satisfied the criteria (slope: 1±0.1, intercept: 0±0.5). A relative error analysis between the PM₁₀ Gravimetric method and β-ray attenuation samplers equipment showed that the relative error increased when the concentration was low and the temperature and humidity were high. In addition, in the β-ray attenuation samplers 5-minute interval observation data in May 2020, a relatively large Standard devication was shown as an average maximum ±23.4 ㎍/m³ and a minimum ±15.2 ㎍/m³. At standard deviations of 10% and 90%, equipment with high variability (deviation) was measured at 6 ㎍/m³and 61 ㎍/m³, and equipment with low variability was measured at 12 ㎍/m³ and 47 ㎍/m³. It was confirmed that concentration differences occurred due to differences in variability for each equipment.

• Korean Journal of Agricultural and Forest Meteorology
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• v.12 no.4
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• pp.217-224
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• 2010

This study was carried out to quantify potential effects of the surrounding ocean on the observed air temperature at coastal weather stations in the Korean Peninsula. Daily maximum and minimum temperature data for 2001-2009 were collected from 66 Korea Meteorological Administration (KMA) stations and the monthly averages were calculated for further analyses. Monthly data from 27 inland sites were used to generate a gridded temperature surface for the whole Peninsula based on an inverse distance weighting and the local temperature at the remaining 39 sites were estimated by recent techniques in geospatial climatology which are widely used in correction of small - scale climate controls like cold air drainage, urban heat island, topography as well as elevation. Deviations from the observed temperature were regarded as the 'apparent' sea effect and showed a quasi-logarithmic relationship with the distance of each site from the nearest coastline. Potential effects of the sea on daily temperature might exceed $6.0^{\circ}C$ cooling in summer and $6.5^{\circ}C$ warming in winter according to this relationship. We classified 25 sites within the 10 km distance from the nearest coastline into 'coastal sites' and the remaining 15 'fringe sites'. When the average deviations of the fringe sites ($0.5^{\circ}C$ for daily maximum and $1.0^{\circ}C$ for daily minimum temperature) were used as the 'noise' and subtracted from the 'apparent' sea effects of the coastal sites, maximum cooling effects of the sea were identified as $1.5^{\circ}C$ on the west coast and $3.0^{\circ}C$ on the east and the south coast in summer months. The warming effects of the sea in winter ranged from $1.0^{\circ}C$ on the west and $3.5^{\circ}C$ on the south and east coasts.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.183-185
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• 2015

Recently, big data analysis based on machine learning has gained popularity and many machine learning techniques have been applied to the field of agriculture. By using machine learning technique to analyze huge number of samples of biological and environmental data, new observations can be found. In this research, we consider the estimation of proportional daily weight increase (PDWI) based on measurement data from experimental swine farm. In order to derive the exact formulation for PDWI estimation, we have used measured value of mean, daily maximum, daily minimum of temperature, humidity, CO2, wind speed and measured PDWI values. Based on collected data, we have derived equation for PDWI estimation using tree-based algorithm. In the derived formulation, we have found that the daily average temperature is the most dominant factor that affects PDWI. Our results can be applied to pig farms to estimate the PDWI of swine.

• Journal of the Korean Society for Geothermal and Hydrothermal Energy
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• v.12 no.1
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• pp.7-16
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• 2016

This paper presents the measurement and analysis results for the cooling performance of ground-coupled heat pump (GCHP) system using a cooling tower as a supplemental heat rejector. In order to demonstrate the performance of the hybrid approach, we installed the monitoring equipments including sensors for measuring temperature and power consumption, and measured operation parameters from May 1 to October 30, 2014. The results showed that the entering source temperature of brine returning from the ground heat exchanger was in a range of design target temperature. Leaving load temperatures to building showed an average value of $11.4^{\circ}C$ for cooling season. From the analysis, the daily performance factor (PF) of geothermal heat pumps ranged from 4.4 to 5.2, while the daily PF of hybrid GCHP system varied from 3.0 to 4.0 over the entire cooling season.