• KIPS Transactions on Software and Data Engineering
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• v.12 no.3
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• pp.133-140
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• 2023

The performance of lithium ion batteries depends on the usage environment and the combination ratio of cathode materials. In order to develop a high-performance lithium-ion battery, it is necessary to manufacture the battery and measure its performance while varying the cathode material ratio. However, it takes a lot of time and money to directly develop batteries and measure their performance for all combinations of variables. Therefore, research to predict the performance of a battery using an artificial intelligence model has been actively conducted. However, since measurement experiments were conducted with the same battery in the existing published battery data, the cathode material combination ratio was fixed and was not included as a data attribute. In this paper, we define a training data model required to develop an artificial intelligence model that can predict battery performance according to the combination ratio of cathode materials. We analyzed the factors that can affect the performance of lithium-ion batteries and defined the mass of each cathode material and battery usage environment (cycle, current, temperature, time) as input data and the battery power and capacity as target data. In the battery data in different experimental environments, each battery data maintained a unique pattern, and the battery classification model showed that each battery was classified with an error of about 2%.

• The Korean Journal of Nuclear Medicine Technology
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• v.21 no.2
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• pp.44-48
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• 2017

Purpose Estradiol (E2) is a steroid hormone mainly produced in women and is a useful indicator for diagnosis of gynecological diseases, menstrual cycle, menopause, and precocious puberty. E2 measurement is performed by diluting the $^{125}I$ radioactive tracer and tracer buffer in the kit. However, It was not precisely specified when the period of tracer is available after activating. The purpose of this study was to determine the appropriate dilution time based on the measurement value with dilution time. Materials and Methods From December 2016 to February 2017, 60 E2 samples with concentrations ranging from 8 to 4577 pg/mL were divided into low, medium, and high concentrations. Dilution of the $^{125}I$ tracer was performed on a 230 RPM agitator for 30 minutes, 1 hour 30 minutes, and 2 hours 30 minutes, respectively. 24 hour dilution was gently shaken and refrigerated. To verify the difference and significance of the results according to the dilution time, a test of normality was performed using SPSS 18.0 and analyzed by Kruskal-Wallis test. The measured value according to the dilution time was compared with the interquartile range of the absolute error. Results The results of Kruskal-Wallis test were not significant (P>0.05). Measurement results are showed as interquartile range of absolute error. At low concentration, it is 0.052 between 1 hour 30 minutes and 2 hours 30 minutes, and 0.105 between 30 minutes and 1 hour 30 minutes. At medium concentration, 0.062 between 30 minutes and 1 hour 30 minutes, and 0.038 between 1 hour 30 minutes and 2 hours 30 minutes. At high concentration, it is 0.029 between 1 hour 30 minutes and 2 hours 30 minutes, and 0.06 between 2 hours 30 minutes and 24 hours. Conclusion There were no statistically significant differences. However, the change in the measured value is the smallest between 1 hour and 30 minutes to 2 hours and 30 minutes. Therefore, we recommend diluting time between 1 hour 30 minutes and 2 hours 30 minutes.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.2
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• pp.87-95
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• 2016

This study examined the characteristics of the hourly demand of city gas in Korea and proposed multiple regression models to obtain precise estimates of the hourly demand of city gas. Forecasting the hourly demand of city gas with accuracy is essential in terms of safety and cost. If underestimated, the pipeline pressure needs to be increased sharply to meet the demand, when safety matters. In the opposite case, unnecessary inventory and operation costs are incurred. Data analysis showed that the hourly demand of city gas has a very high autocorrelation and that the 24-hour demand pattern of a day follows the previous 24-hour demand pattern of the same day. That is, there is a weekly cycle pattern. In addition, some conditions that temperature affects the hourly demand level were found. That is, the absolute value of the correlation coefficient between the hourly demand and temperature is about 0.853 on average, while the absolute value of the correlation coefficient on a specific day improves to 0.861 at worst and 0.965 at best. Based on this analysis, this paper proposes a multiple regression model incorporating the hourly demand ahead of 24 hours and the hourly demand ahead of 168 hours, and another multiple regression model with temperature as an additional independent variable. To show the performance of the proposed models, computational experiments were carried out using real data of the domestic city gas demand from 2009 to 2013. The test results showed that the first regression model exhibits a forecasting accuracy of MAPE (Mean Absolute Percentage Error) around 4.5% over the past five years from 2009 to 2013, while the second regression model exhibits 5.13% of MAPE for the same period.

• The Journal of Korean Society for Radiation Therapy
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• v.22 no.2
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• pp.113-122
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• 2010

Purpose: To evaluate the accuracy of a target position at static and dynamic state by using Dynamic phantom for the difference between tumor's actual movement during respiratory gated radiation therapy and skin movement measured by RPM (Real-time Position Management). Materials and Methods: It self-produced Dynamic phantom that moves two-dimensionally to measure a tumor moved by breath. After putting marker block on dynamic phantom, it analyzed the amplitude and status change depending on respiratory time setup in advance by using RPM. It places marker block on dynamic phantom based on this result, inserts Gafchromic EBT film into the target, and investigates 5 Gy respectively at static and dynamic state. And it scanned investigated Gafchromic EBT film and analyzed dose distribution by using automatic calculation. Results: As a result of an analysis of Gafchromic EBT film's radiation amount at static and dynamic state, it could be known that dose distribution involving 90% is distributed within margin of error of 3 mm. Conclusion: As a result of an analysis of dose distribution's change depending on patient's respiratory cycle during respiratory gated radiation therapy, it is expected that the treatment would be possible within recommended margin of error at ICRP 60.

• Progress in Medical Physics
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• v.26 no.2
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• pp.87-92
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• 2015

The gated RapidArc may produce a dosimetric error due to the stop-and-go motion of heavy gantry which can misalign the gantry restart position and reduce the accuracy of important factors in RapidArc delivery such as MLC movement and gantry speed. In this study, the effect of stop-and-go motion in gated RapidArc was analyzed with varying gating window time, which determines the total number of stop-and-go motions. Total 10 RapidArc plans for treatment of liver cancer were prepared. The RPM gating system and the moving phantom were used to set up the accurate gating window time. Two different delivery quality assurance (DQA) plans were created for each RapidArc plan. One is the portal dosimetry plan and the other is MapCHECK2 plan. The respiratory cycle was set to 4 sec and DQA plans were delivered with three different gating conditions: no gating, 1-sec gating window, and 2-sec gating window. The error between calculated dose and measured dose was evaluated based on the pass rate calculated using the gamma evaluation method with 3%/3 mm criteria. The average pass rates in the portal dosimetry plans were $98.72{\pm}0.82%$, $94.91{\pm}1.64%$, and $98.23{\pm}0.97%$ for no gating, 1-sec gating, and 2-sec gating, respectively. The average pass rates in MapCHECK2 plans were $97.80{\pm}0.91%$, $95.38{\pm}1.31%$, and $97.50{\pm}0.96%$ for no gating, 1-sec gating, and 2-sec gating, respectively. We verified that the dosimetric accuracy of gated RapidArc increases as gating window time increases and efforts should be made to increase gating window time during the RapidArc treatment process.

• Korean Journal of Remote Sensing
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• v.35 no.6_2
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• pp.1117-1132
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• 2019

Forest covers 30% of the Earth's land area and plays an important role in global carbon flux through its ability to store much greater amounts of carbon than other terrestrial ecosystems. The Gross Primary Production (GPP) represents the productivity of forest ecosystems according to climate change and its effect on the phenology, health, and carbon cycle. In this study, we estimated the daily GPP for a forest ecosystem using remote-sensed data from Moderate Resolution Imaging Spectroradiometer (MODIS) and machine learning algorithms Support Vector Machine (SVM). MODIS products were employed to train the SVM model from 75% to 80% data of the total study period and validated using eddy covariance measurement (EC) data at the six flux tower sites. We also compare the GPP derived from EC and MODIS (MYD17). The MODIS products made use of two data sets: one for Processed MODIS that included calculated by combined products (e.g., Vapor Pressure Deficit), another one for Unprocessed MODIS that used MODIS products without any combined calculation. Statistical analyses, including Pearson correlation coefficient (R), mean squared error (MSE), and root mean square error (RMSE) were used to evaluate the outcomes of the model. In general, the SVM model trained by the Unprocessed MODIS (R = 0.77 - 0.94, p < 0.001) derived from the multi-sites outperformed those trained at a single-site (R = 0.75 - 0.95, p < 0.001). These results show better performance trained by the data including various events and suggest the possibility of using remote-sensed data without complex processes to estimate GPP such as non-stationary ecological processes.

• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.233-246
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• 2023

In terms of understanding the water cycle and efficient water resource management, the importance of soil moisture has been highlighted. However, in Korea, the lack of qualified in-situ soil moisture data results in very limited utility. Even if satellite-based data are applied, the absence of ground reference data makes objective evaluation and correction difficult. The cosmic-ray neutron probe (CRNP) can play a key role in producing data for satellite data calibration. The installation of CRNP is non-invasive, minimizing damage to the soil and vegetation environment, and has the advantage of having a spatial representative for the intermediate scale. These characteristics are advantageous to establish an observation network in Korea which has lots of mountainous areas with dense vegetation. Therefore, this study was conducted to evaluate the applicability of the CRNP soil moisture observatory in Korea as part of the establishment of a Korean cOsmic-ray Soil Moisture Observing System (KOSMOS). The CRNP observation station was installed with the Gunup-ri observation station, considering the ease of securing power and installation sites and the efficient use of other hydro-meteorological factors. In order to evaluate the CRNP soil moisture data, 12 additional in-situ soil moisture sensors were installed, and spatial representativeness was evaluated through a temporal stability analysis. The neutrons generated by CRNP were found to be about 1,087 counts per hour on average, which was lower than that of the Solmacheon observation station, indicating that the Hongcheon observation station has a more humid environment. Soil moisture was estimated through neutron correction and early-stage calibration of the observed neutron data. The CRNP soil moisture data showed a high correlation with r=0.82 and high accuracy with root mean square error=0.02 m³/m³ in validation with in-situ data, even in a short calibration period. It is expected that higher quality soil moisture data production with greater accuracy will be possible after recalibration with the accumulation of annual data reflecting seasonal patterns. These results, together with previous studies that verified the excellence of CRNP soil moisture data, suggest that high-quality soil moisture data can be produced when constructing KOSMOS.

• Journal of the Microelectronics and Packaging Society
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• v.31 no.1
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• pp.16-22
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• 2024

Along with the high interest in electric vehicles and new renewable energy, there is a growing demand to apply lithium-ion batteries in the construction equipment industry. The capacity of heavy construction equipment that performs various tasks at construction sites is rapidly decreasing. Therefore, it is essential to accurately predict the state of batteries such as SOC (State of Charge) and SOH (State of Health). In this paper, the errors between actual electrochemical measurement data and estimated data were compared using the Dual Extended Kalman Filter (DEKF) algorithm that can estimate SOC and SOH at the same time. The prediction of battery charge state was analyzed by measuring OCV at SOC 5% intervals under 0.2C-rate conditions after the battery cell was fully charged, and the degradation state of the battery was predicted after 50 cycles of aging tests under various C-rate (0.2, 0.3, 0.5, 1.0, 1.5C rate) conditions. It was confirmed that the SOC and SOH estimation errors using DEKF tended to increase as the C-rate increased. It was confirmed that the SOC estimation using DEKF showed less than 6% at 0.2, 0.5, and 1C-rate. In addition, it was confirmed that the SOH estimation results showed good performance within the maximum error of 1.0% and 1.3% at 0.2 and 0.3C-rate, respectively. Also, it was confirmed that the estimation error also increased from 1.5% to 2% as the C-rate increased from 0.5 to 1.5C-rate. However, this result shows that all SOH estimation results using DEKF were excellent within about 2%.

• Korean Journal of Agricultural Science
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• v.8 no.2
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• pp.195-202
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• 1981

The return of water to the atmosphere from water, soil and vegetation surface is one of the most important aspects of hydrological cycle, and the seasonal trend of variation of river basin evaporation is also meaningful in the longterm runoff analysis for the irrigation and water resources planning. This paper has been prepared to show some imformation to estimate the monthly river basin evaporation from pan evaporation, potential evaporation, regional evaporation and temperature through the comparison with river basin evaporation derived from water budget method. The analysis has been carried out with the observation data of Yongdam station in the Geum river basin for five year. The results are summarized as follows and these would be applied to the estimation of river basin evaporation and longterm runoff in ungaged station. 1. The ratio of pan evaporation to river basin evaporation ($E_w/E_{pan}$) shows the most- significant relation at the viewpoint of seasonal trend of variation. River basin evaporation could be estimated from the pan evaporation through either Fig. 9 or Table-7. 2. Local coefficients of cloudness effect and wind function has been determined to apply the Penman's mass and energy transfer equation to the estimation of river basin evaporation. $R_c=R_a(0.13+0.52n/D)$ $E=0.35(e_s-e)(1.8+1.0U)$ 3. It seems that Regional evaporation concept $E_R=(1-a)R_C-E_p$ has kept functional errors due to the inapplicable assumptions. But it is desirable that this kind of function which contains the results of complex physical, chemical and biological processes of river basin evaporation should be developed. 4. Monthly river basin evaporation could be approximately estimated from the monthly average temperature through either the equation of $E_w=1.44{\times}1.08^T$ or Fig. 12 in the stations with poor climatological observation data.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.21 no.1
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• pp.1-10
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• 2016

Currently available surface seawater partial pressure carbon dioxide ($pCO_2$) data sets in the East Sea are not enough to quantify statistically the carbon dioxide flux through the air-sea interface. To complement the scarcity of the $pCO_2$ measurements, we construct a neural network (NN) model based on satellite data to map $pCO_2$ for the areas, which were not observed. The NN model is constructed for the Ulleung Basin, where $pCO_2$ data are best available, to map and estimate the variability of $pCO_2$ based on in situ $pCO_2$ for the years from 2003 to 2012, and the sea surface temperature (SST) and chlorophyll data from the MODIS (Moderate-resolution Imaging Spectroradiometer) sensor of the Aqua satellite along with geographic information. The NN model was trained to achieve higher than 95% of a correlation between in situ and predicted $pCO_2$ values. The RMSE (root mean square error) of the NN model output was $19.2{\mu}atm$ and much less than the variability of in situ $pCO_2$. The variability of $pCO_2$ with respect to SST and chlorophyll shows a strong negative correlation with SST than chlorophyll. As SST decreases the variability of $pCO_2$ increases. When SST is lower than $15^{\circ}C$, $pCO_2$ variability is clearly affected by both SST and chlorophyll. In contrast when SST is higher than $15^{\circ}C$, the variability of $pCO_2$ is less sensitive to changes in SST and chlorophyll. The mean rate of the annual $pCO_2$ increase estimated by the NN model output in the Ulleung Basin is $0.8{\mu}atm\;yr^{-1}$ from 2003 to 2014. As NN model can successfully map $pCO_2$ data for the whole study area with a higher resolution and less RMSE compared to the previous studies, the NN model can be a potentially useful tool for the understanding of the carbon cycle in the East Sea, where accessibility is limited by the international affairs.