• International Journal of Advanced Culture Technology
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• v.11 no.3
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• pp.310-314
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• 2023

The purpose of this study is to predict the remaining capacity of lithium-ion batteries and evaluate their performance using five artificial intelligence models, including linear regression analysis, decision tree, random forest, neural network, and ensemble model. We is in the study, measured Excel data from the CS2 lithium-ion battery was used, and the prediction accuracy of the model was measured using evaluation indicators such as mean square error, mean absolute error, coefficient of determination, and root mean square error. As a result of this study, the Root Mean Square Error(RMSE) of the linear regression model was 0.045, the decision tree model was 0.038, the random forest model was 0.034, the neural network model was 0.032, and the ensemble model was 0.030. The ensemble model had the best prediction performance, with the neural network model taking second place. The decision tree model and random forest model also performed quite well, and the linear regression model showed poor prediction performance compared to other models. Therefore, through this study, ensemble models and neural network models are most suitable for predicting the remaining capacity of lithium-ion batteries, and decision tree and random forest models also showed good performance. Linear regression models showed relatively poor predictive performance. Therefore, it was concluded that it is appropriate to prioritize ensemble models and neural network models in order to improve the efficiency of battery management and energy systems.

• Geomechanics and Engineering
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• v.30 no.5
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• pp.437-448
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• 2022

Alluvial soil is challenging to work with due to its high compressibility. Thus, consolidation settlement of this type of soil should be accurately estimated. Accurate estimation of the consolidation settlement of alluvial soil requires accurate prediction of compressibility parameters. Geotechnical engineers usually use empirical correlations to estimate these compressibility parameters. However, no attempts have been made to develop correlations to estimate compressibility parameters of alluvial soil. Thus, this paper aims to develop new models to predict the compression and recompression indices (Cc and Cr) of alluvial soils. As part of the study, geotechnical laboratory tests have been conducted on large number of undisturbed samples of local alluvial soil. The obtained results from these tests in addition to available results from the literature from different parts in the world have been compiled to form the database of this study. This database is then employed to examine the accuracy of the available empirical correlations of the compressibility parameters and to develop the new models to estimate the compressibility parameters using the nonlinear regression analysis. The accuracy of the new models has been accessed using mean absolute error, root mean square error, mean, percentage of predictions with error range of ±20%, percentage of predictions with error range of ±30%, and coefficient of determination. It was found that the new models outperform the available correlations. Thus, these models can be used by geotechnical engineers with more confidence to predict Cc and Cr.

• Journal of Animal Science and Technology
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• v.64 no.3
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• pp.531-538
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• 2022

In Italy, buffalo mozzarella is a largely sold and consumed dairy product. The fraudulent adulteration of buffalo milk with cheaper and more available milk of other species is very frequent. In the present study, Fourier transform infrared spectroscopy (FTIR), in combination with multivariate analysis by partial least square (PLS) regression, was applied to quantitatively detect the adulteration of buffalo milk with cow milk by using a fully automatic equipment dedicated to the routine analysis of the milk composition. To enhance the heterogeneity, cow and buffalo bulk milk was collected for a period of over three years from different dairy farms. A total of 119 samples were used for the analysis to generate 17 different concentrations of buffalo-cow milk mixtures. This procedure was used to enhance variability and to properly randomize the trials. The obtained calibration model showed an R² ≥ 0.99 (R² cal. = 0.99861; root mean square error of cross-validation [RMSEC] = 2.04; R² val. = 0.99803; root mean square error of prediction [RMSEP] = 2.84; root mean square error of cross-validation [RMSECV] = 2.44) suggesting that this method could be successfully applied in the routine analysis of buffalo milk composition, providing rapid screening for possible adulteration with cow's milk at no additional cost.

• Journal of Environmental Science International
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• v.31 no.7
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• pp.555-567
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• 2022

With the introduction of the tele-monitoring system (TMS) in South Korea, monitoring of the concentration of pollutants discharged from nationwide water quality TMS attachments is possible. In addition, the Ministry of Environment is implementing a smart sewage system program that combines ICT technology with wastewater treatment plants. Thus, many institutions are adopting the automatic operation technique which uses process operation factors and TMS data of sewage treatment plants. As a part of the preliminary study, a multilayer perceptron (MLP) analysis method was applied to TMS data to identify predictability degree. TMS data were designated as independent variables, and each pollutant was considered as an independent variables. To verify the validity of the prediction, root mean square error analysis was conducted. TMS data from two public sewage treatment plants in Chungnam were used. The values of RMSE in SS, T-N, and COD predictions (excluding T-P) in treatment plant A showed an error range of 10%, and in the case of treatment plant B, all items showed an error exceeding 20%. If the total amount of data used MLP analysis increases, the predictability of MLP analysis is expected to increase further.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2010.04a
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• pp.253-255
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• 2010

Needs to study on distance measurement accuracy in aerial and satellite photogrammetry are rapidly increased. However, conventional studies show some confused definitions between measurement accuracy and measurement precision as well as standard deviation(STDEV) and root mean square error(RMSE or RMSD). So, Finite definitions of measurement accuracy and measurement precision as well as STDEV and RMSD are addressed in this paper. Experiment result show using correct definitions improve the distance measurement accuracy in aerial and satellite photogrammetry rapidly, but not the distance measurement accuracy in aerial and satellite photogrammetry.

• KCID journal
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• v.19 no.1
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• pp.87-96
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• 2012

Information of local solar radiation is essential for many field, including water resources management, crop yield estimation, crop growth model, solar energy systems and irrigation and drainage design. Unfortunately, solar radiation measurements are not easily available due to the cost and maintenance and calibration requirements of the measuring equipment and station. Therefore, it is important to elaborate methods to estimate the solar radiation based on readily available meteorological data. In this study, two empirical equations are employed to estimate daily solar radiation using Cheongju Regional Meteorological Office data. Two scenarios are considered: (a) sunshine duration data are available for a given location, or (b) only daily cloudiness index records exist. Simple linear regression with daily sunshine duration and cloudiness index as the dependent variable accounted for 91% and 80%, respectively of the variation of solar radiation(H) at 2011. Daily global solar radiation is highly correlated with sunshine duration. In order to indicate the performance of the models, the statistical test methods of the mean bias error(MBE), root mean square error(RMSE) and correlation coefficient(r) are used. Sunshine duration and cloudiness index can be easily and reliably measured and data are widely available.

• Journal of Korea Water Resources Association
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• v.39 no.8 s.169
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• pp.677-690
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• 2006

The LH-OAT (Latin Hypercube One factor At a Time) method for sensitivity analysis and SCE-UA (Shuffled Complex Evolution at University of Arizona) optimization method were applied for the automatic calibration of SWAT model in Bocheong-cheon watershed. The LH-OAT method which combines the advantages of global and local sensitivity analysis effectively identified the sensitivity ranking for the parameters of SWAT model over feasible parameter space. Use of this information allows us to select the calibrated parameters for the automatic calibration process. The performance of the automatic calibration of SWAT model using SCE-UA method depends on the length of calibration period, the number of calibrated parameters, and the selection of statistical error criteria. The performance of SWAT model in terms of RMSE (Root Mean Square Error), NSEF (Nash-Sutcliffe Model Efficiency), RMAE (Relative Mean Absolute Error), and NMSE (Normalized Mean Square Error) becomes better as the calibration period and the number of parameters defined in the automatic calibration process increase. However, NAE (Normalized Average Error) and SDR (Standard Deviation Ratio) were not improved although the calibration period and the number of calibrated parameters are increased. The result suggests that there are complex interactions among the calibration data, the calibrated parameters, and the model error criteria and a need for further study to understand these complex interactions at various representative watersheds.

• Communications for Statistical Applications and Methods
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• v.30 no.3
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• pp.273-289
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• 2023

In this paper, we develop a new time series model for predicting IPO (initial public offering) data with non-negative integer value. The proposed model is based on integer-valued autoregressive (INAR) model with a Poisson thinning operator. Just as the heterogeneous autoregressive (HAR) model with daily, weekly and monthly averages in a form of cascade, the integer-valued heterogeneous autoregressive (INHAR) model is considered to reflect efficiently the long memory. The parameters of the INHAR model are estimated using the conditional least squares estimate and Yule-Walker estimate. Through simulations, bias and standard error are calculated to compare the performance of the estimates. Effects of model fitting to the Korea's IPO are evaluated using performance measures such as mean square error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE) etc. The results show that INHAR model provides better performance than traditional INAR model. The empirical analysis of the Korea's IPO indicates that our proposed model is efficient in forecasting monthly IPO volumes.

• Transactions of Materials Processing
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• v.29 no.5
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• pp.282-289
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• 2020

Flexible roll forming is an advanced sheet-metal-forming process that allows the production of parts with various cross-sections. During the flexible process, material is subjected to three-dimensional deformation such as transverse bending, inhomogeneous elongations, or contraction. Because of the effects of process variables on the quality of the roll-formed products, the approaches used to investigate the roll-forming process have been largely dependent on experience and trial- and-error methods. Web-warping is one of the major shape defects encountered in flexible roll forming. In this study, an SVR model was developed to predict the web-warping during the flexible roll forming process. In the development of the SVR model, three process parameters, namely the forming-roll speed condition, leveling-roll height, and bend angle were considered as the model inputs, and the web-warping height was used as the response variable for three blank shapes; rectangular, concave, and convex shape. MATLAB software was used to train the SVR model and optimize three hyperparameters (λ, ε, and γ). To evaluate the SVR model performance, the statistical analysis was carried out based on the three indicators: the root-mean-square error, mean absolute error, and relative root-mean-square error.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.11
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• pp.1406-1409
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• 2016

In this paper, we propose the heuristic signal grouping algorithm to estimate channel state value over full blind communication situation which means that there is no information about the modulation scheme and the channel state information between the transmitter and the receiver. Hereafter, using the constellation rotation method and the probability density function(pdf) the modulation scheme is determined to perform automatic modulation classification(AMC). Furthermore, the modulation type and a channel state value estimation capability is evaluated by comparing the proposed scheme with other conventional techniques from the simulation results in terms of the symbol error rate(SER) and the root mean square error (RMSE).