• The Journal of the Convergence on Culture Technology
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• v.9 no.3
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• pp.727-730
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• 2023

The operation of electric automatic windows is used in harsh environments, and the energy density decreases as charging and discharging are repeated, and as soundness deteriorates due to damage to the internal separator, the vehicle's mileage decreases and the charging speed slows down, so about 5 to 10 Batteries that have been used for about a year are classified as waste batteries, and for this reason, as the risk of battery fire and explosion increases, it is essential to diagnose batteries and estimate SOH. Estimation of current battery SOH is a very important content, and it evaluates the state of the battery by measuring the time, temperature, and voltage required while repeatedly charging and discharging the battery. There are disadvantages. In this paper, measurement of discharge capacity (C-rate) using a waste battery of a Tesla car in order to predict SOH estimation of a lithium-ion battery. A Support Vector Machine (SVM), one of the machine models, was applied using the data measured from the waste battery.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1269-1276
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• 2023

Crop estimation is essential for the multinational meal and powerful demand due to its numerous aspects like soil, rain, climate, atmosphere, and their relations. The consequence of climate shift impacts the farming yield products. We operate the dataset with temperature, rainfall, humidity, etc. The current research focuses on feature selection with multifarious classifiers to assist farmers and agriculturalists. The crop yield estimation utilizing the feature selection approach is 96% accuracy. Feature selection affects a machine learning model's performance. Additionally, the performance of the current graph classifier accepts 81.5%. Eventually, the random forest regressor without feature selections owns 78% accuracy and the decision tree regressor without feature selections retains 67% accuracy. Our research merit is to reveal the experimental results of with and without feature selection significance for the proposed ten algorithms. These findings support learners and students in choosing the appropriate models for crop classification studies.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.8
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• pp.2431-2449
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• 2024

As problems such as water pollution and fish species depletion have become serious, a land-based fish farming is receiving a great attention for ensuring stable productivity. In the fish farming, it is important to determine the timing of shipments, as one of key factors to increase net profit on the aquaculture. In this paper, we propose a system for predicting net profit to support decision of timing of shipment using fish farming-related statistical data. The prediction system consists of growth and farm-gate price prediction models, a cost statistics table, and a net profit estimation algorithm. The Gaussian process regression (GPR) model is exploited for weight prediction based on the analysis that represents the characteristics of the weight data of cultured fish under the assumption of Gaussian probability processes. Moreover, the long short-term memory (LSTM) model is applied considering the simple time series characteristics of the farm-gate price data. In the case of GPR model, it allows to cope with data missing problem of the weight data collected from the fish farm in the time and temperature domains. To solve the problem that the data acquired from the fish farm is aperiodic and small in amount, we generate the corresponding data by adopting a data augmentation method based on the Gaussian model. Finally, the estimation method for net profit is proposed by concatenating weight, price, and cost predictions. The performance of the proposed system is analyzed by applying the system to the Korean flounder data.

• Journal of Korea Water Resources Association
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• v.52 no.12
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• pp.1033-1046
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• 2019

To evaluate the utilization suitability of solar radiation models, estimated solar radiation from 13 solar radiation models were verified by comparing with measured solar radiation at 5 study stations in South Korea. Furthermore, for the evaluation of evaporation estimates according to solar radiation models, 5 different evaporation estimation equations based on Penman's combination approach were applied, and evaporation estimates were compared with pan evaporation. Some solar radiation models require only meteorological data; however, some other models require not only meteorological data but also geographical data such as elevation. The study results showed that solar radiation model based on the ratio of the duration of sunshine to the possible duration of sunshine, maximum temperature, and minimum temperature provided the estimated solar radiation that most closely match measured solar radiation. Accuracy of estimated solar radiation also greatly improved when Angstrőm-Prescott model coefficients are adjusted to the study stations. Therefore, when choosing the solar radiation model for evaporation estimation, both data availability and model capability should be considered simultaneously. When applying measured solar radiation for estimating evaporation, evaporation estimates from Penman, FAO Penman-Monteith, and KNF equations are most close to pan evaporation rates in Jeonju and Jeju, Seoul and Mokpo, and Daejeon respectively.

• Korean Journal of Remote Sensing
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• v.36 no.3
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• pp.411-423
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• 2020

Localized solar irradiance is normally derived from atmospheric transmission influenced by atmospheric composition and conditions of the target area. Specially, for the area with complex coastal lines such as Taean gun, the accurate estimation of solar irradiance requires for in depth analysis of atmospheric transmission characteristics based on the localized vertical profiles of the key atmospheric parameters. Using MODTRAN (MODerate resolution atmospheric TRANsmission) 6, we report a computational study on clear day atmospheric transmission and direct solar irradiance estimation of Taean gun using the data collected from 3 ground stations and radiosonde measurement over 93 clear days in 2018. The MODTRAN estimated direct solar irradiance is compared with the measurement. The results show that the normalized residual mean (NRM) is 0.28 for the temperature based MODTRAN atmospheric model and 0.32 for the pressure based MODTRAN atmospheric model. These values are larger than 0.1~0.2 of the other study and we understand that such difference represents the local atmospheric characteristics of Taean gun. The results also show that NRM tends to increase noticeably in summer as the temperature increases. Such findings from this study can be very useful for estimation and prediction of the atmospheric condition of the local area with complex coastal lines.

• Journal of Korea Water Resources Association
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• v.38 no.8 s.157
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• pp.617-629
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• 2005

In this paper, a hypothesis is tested that division of non-monotonic time series into monotonic parts will improve the estimation of trends through increased homogeneity in direction of time-variation using LOWESS smoothing and seasonal Kendall test. From the trend analysis of generated time series and water temperature, discharge, air temperature and solar radiation of Lake Daechung, it is shown that the hypothesis is supported by improved estimation of trends and slopes. Also, characteristics in homogeneity variation of seasonal changes seems to be more clearly manifested as homogeneity in direction of time-variation is increased. And this will help understand the effects of human intervention on natural processes and seems to warrant more in-depth study on this subject. The proposed method can be used for trend analysis to detect monotonic trends and it is expected to improve understanding of long-term changes in natural environment.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.164-175
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• 2019

The Tor Tong Daeng Irrigation Project with the irrigation area of 61,400 hectares is located in the Ping Basin of the Upper Central Plain of Thailand where farmers depended on both surface water and groundwater. In the drought year, water storage in the Bhumipol Dam is inadequate to allocate water for agriculture, and caused water deficit in many irrigation projects. Farmers need to find extra sources of water such as water from farm pond or groundwater as a supplement. The operation of Bhumipol Dam and irrigation demand estimation are vital for irrigation water allocation to help solve water shortage issue in the irrigation project. The study aims to determine the smart dam operation system to mitigate water shortage in this irrigation project via introduction of machine learning to improve dam operation and irrigation demand estimation via soil moisture estimation from satellite images. Via ANN technique application, the inflows to the dam are generated from the upstream rain gauge stations using past 10 years daily rainfall data. The input vectors for ANN model are identified base on regression and principal component analysis. The structure of ANN (length of training data, the type of activation functions, the number of hidden nodes and training methods) is determined from the statistics performance between measurements and ANN outputs. On the other hands, the irrigation demand will be estimated by using satellite images, LANDSAT. The Enhanced Vegetation Index (EVI) and Temperature Vegetation Dryness Index (TVDI) values are estimated from the plant growth stage and soil moisture. The values are calibrated and verified with the field plant growth stages and soil moisture data in the year 2017-2018. The irrigation demand in the irrigation project is then estimated from the plant growth stage and soil moisture in the area. With the estimated dam inflow and irrigation demand, the dam operation will manage the water release in the better manner compared with the past operational data. The results show how smart system concept was applied and improve dam operation by using inflow estimation from ANN technique combining with irrigation demand estimation from satellite images when compared with the past operation data which is an initial step to develop the smart dam operation system in Thailand.

• Korean Journal of Agricultural and Forest Meteorology
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• v.8 no.4
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• pp.250-259
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• 2006

This study looks into forest fire occurrence hazards according to the change of temperature and humidity over thirty years at interval of ten days. We used data from the forest fire inventory from 1995 to 2004 and weather data such as average temperature and relative humidity for 30 years from 1971 to 2000. These data were expressed as a database with ten-day intervals for 76 weather stations. Forest fire hazards occurred in the spring season from the end of March to the middle of April. For the first step, the primitive surface of temperature and humidity was interpolated by IDW (the standard interpolation method). These thematic maps have a 1 km by 1 km grid spacing resolution. Next, we executed a simple regression analysis after extracting forest fire frequency, temperature and humidity values from 76 weather stations. The results produced a coefficient of determination ($R^2$) ranging from 0.4 to 0.6. Moreover, the estimation of forest fire occurrence hazards during early April was very high at Gyeongbuk Interior, Chungcheong Interior and part of Gangwon. The range of temperature and humidity having an influence on forest fire occurrence was as follows: average temperature and relative humidity in early April was $9-12^{\circ}C$ and 61-65%. At the end of March, temperature was $6-10^{\circ}C$, humidity 62-67%, and temperature was $11-14^{\circ}C$ and humidity 60-67% in the middle of April.

• Korean Journal of Agricultural and Forest Meteorology
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• v.14 no.4
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• pp.155-160
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• 2012

This study was conducted to develop a practical method for estimating the extremes of minimum air temperature with given return-period based on the frequency distribution of daily minimum air temperature in January. Daily temperature data were collected from 61 meteorological observatories country-wide from 1961 to 2010. Most of daily minimum temperature in January could be represented by a normal-distribution, so it is possible to predict stochastically the lowest temperature by the mean and standard deviation. We developed a quadratic function to estimate standard deviation in terms of daily minimum temperature in January. Also, we introduced a coefficient which can be used to predict an extreme of minimum temperature with mean and standard deviation, and is dependent on return-periods. Using this method, we were able to reproduce the past 30-year extremes with an error of 1.1 on average and 5.3 in the worst case.

• Korean Journal of Agricultural and Forest Meteorology
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• v.19 no.3
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• pp.93-101
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• 2017

The distribution of inter-annual variation in temperature would help evaluate the likelihood of a climatic risk and assess suitable zones of crops under climate change. In this study, we evaluated two methods to estimate the standard deviation of temperature in the areas where weather information is limited. We calculated the monthly standard deviation of temperature by collecting temperature at 0600 and 1500 local standard time from 10 automated weather stations (AWS). These weather stations were installed in the range of 8 to 1,073m above sea level within a mountainous catchment for 2011-2015. The observed values were compared with estimates, which were calculated using a geospatial correction scheme to derive the site-specific temperature. Those estimates explained 88 and 86% of the temperature variations at 0600 and 1500 LST, respectively. However, it often underestimated the temperatures. In the spring and fall, it tended to had different variance (e.g., increasing or decreasing pattern) from lower to higher elevation with the observed values. A regression analysis was also conducted to quantify the relationship between the standard deviation in temperature and the topography. The regression equation explained a relatively large variation of the monthly standard deviation when lapse-rate corrected temperature, basic topographical variables (e.g., slope, and aspect) and topographical variables related to temperature (e.g., thermal belt, cold air drainage, and brightness index) were used. The coefficient of determination for the regression analysis ranged between 0.46 and 0.98. It was expected that the regression model could account for 70% of the spatial variation of the standard deviation when the monthly standard deviation was predicted by using the minimum-maximum effective range of topographical variables for the area.