Performance Comparison Analysis of Artificial Intelligence Models for Estimating Remaining Capacity of Lithium-Ion Batteries

김규하,정병수,이상현 국제문화기술진흥원 2023 International Journal of Advanced Culture Technolo Vol.11 No.3

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.

Estimation of Genetic Parameters for Milk Production Traits Using a Random Regression Test-day Model in Holstein Cows in Korea

Kim, Byeong-Woo,Lee, Deukhwan,Jeon, Jin-Tae,Lee, Jung-Gyu Asian Australasian Association of Animal Productio 2009 Animal Bioscience Vol.22 No.7

This study was conducted to compare three models: two random regression models with and without considering heterogeneity in the residual variances and a lactation model (LM) for evaluating the genetic ability of Holstein cows in Korea. Two datasets were prepared for this study. To apply the test-day random regression model, 94,390 test-day records were prepared from 15,263 cows. The second data set consisted of 14,704 lactation records covering milk production over 305 days. Raw milk yield and composition data were collected from 1998 to 2002 by the National Agricultural Cooperative Federation' dairy cattle improvement center by way of its milk testing program, which is nationally based. The pedigree information for this analysis was collected by the Korean Animal Improvement Association. The random regression models (RRMs) are single-trait animal models that consider each lactation record as an independent trait. Estimates of covariance were assumed to be different ones. In order to consider heterogeneity of residual variance in the analysis, test-days were classified into 29 classes. By considering heterogeneity of residual variance, variation for lactation performance in the early lactation classes was higher than during the middle classes and variance was lower in the late lactation classes than in the other two classes. This may be due to feeding management system and physiological properties of Holstein cows in Korea. Over classes e6 to e26 (covering 61 to 270 DIM), there was little change in residual variance, suggesting that a model with homogeneity of variance be used restricting the data to these days only. Estimates of heritability for milk yield ranged from 0.154 to 0.455, for which the estimates were variable depending on different lactation periods. Most of the heritabilities for milk yield using the RRM were higher than in the lactation model, and the estimate of genetic variance of milk yield was lower in the late lactation period than in the early or middle periods.

기상 및 토양정보가 고랭지배추 단수예측에 미치는 영향

권태용 ( Taeyong Kwon ),김래용 ( Rae Yong Kim ),윤상후 ( Sanghoo Yoon ) 한국환경과학회 2019 한국환경과학회지 Vol.28 No.8

Highland farming is agriculture that takes place 400 m above sea level and typically involves both low temperatures and long sunshine hours. Most highland Chinese cabbages are harvested in the Gangwon province. The Ubiquitous Sensor Network (USN) has been deployed to observe Chinese cabbages growth because of the lack of installed weather stations in the highlands. Five representative Chinese cabbage cultivation spots were selected for USN and meteorological data collection between 2015 and 2017. The purpose of this study is to develop a weight prediction model for Chinese cabbages using the meteorological and growth data that were collected one week prior. Both a regression and random forest model were considered for this study, with the regression assumptions being satisfied. The Root Mean Square Error (RMSE) was used to evaluate the predictive performance of the models. The variables influencing the weight of cabbage were the number of cabbage leaves, wind speed, precipitation and soil electrical conductivity in the regression model. In the random forest model, cabbage width, the number of cabbage leaves, soil temperature, precipitation, temperature, soil moisture at a depth of 30 cm, cabbage leaf width, soil electrical conductivity, humidity, and cabbage leaf length were screened. The RMSE of the random forest model was 265.478, a value that was relatively lower than that of the regression model (404.493); this is because the random forest model could explain nonlinearity.

Genetic Parameters for Litter Size in Pigs Using a Random Regression Model

Lukovic, Z.,Uremovic, M.,Konjacic, M.,Uremovic, Z.,Vincek, D. Asian Australasian Association of Animal Productio 2007 Animal Bioscience Vol.20 No.2

Dispersion parameters for the number of piglets born alive were estimated using a repeatability and random regression model. Six sow breeds/lines were included in the analysis: Swedish Landrace, Large White and both crossbred lines between them, German Landrace and their cross with Large White. Fixed part of the model included sow genotype, mating season as month-year interaction, parity and weaning to conception interval as class effects. The age at farrowing was modelled as a quadratic regression nested within parity. The previous lactation length was fitted as a linear regression. Random regressions for parity on Legendre polynomials were included for direct additive genetic, permanent environmental, and common litter environmental effects. Orthogonal Legendre polynomials from the linear to the cubic power were fitted. In the repeatability model estimate of heritability was 0.07, permanent environmental effect as ratio was 0.04, and common litter environmental effect as ratio was 0.01. Estimates of genetic parameters with the random regression model were generally higher than in the repeatability model, except for the common litter environmental effect. Estimates of heritability ranged from 0.06 to 0.10. Permanent environmental effect as a ratio increased along a trajectory from 0.03 to 0.11. Magnitudes of common litter effect were small (around 0.01). The eigenvalues of covariance functions showed that between 7 and 8 % of genetic variability was explained by individual genetic curves of sows. This proportion was mainly covered by linear and quadratic coefficients. Results suggest that the random regression model could be used for genetic analysis of litter size.

제주시 아파트시장 하위시장간 이질성과 아파트가격결정요인 -확률계수모형과 위계선형모형을 이용하여

이성원 ( Sung Won Lee ),정수연 ( Su Yeon Jung ) 한국감정평가학회 2015 감정평가학논집 Vol.14 No.1

본 연구는 2012년 제주시 아파트 데이터를 이용하여 무조건부 모형(Unconditional model),확률계수모형 (Random coefficient model), 위계선형모형(Hierarchical linear model)을 분석하였다. 무조건부모형과 확률계수모형의 분석결과, 제주시의 하위시장을 구성하는 14개의 행정동 간에 아파트가격의 차이가존재하는 것을 알 수 있었다. 이 결과가 의미하는 바는 제주시의 하위시장 간에 이질성이 존재한다는것이다. 그리고 이러한 이질성이 존재할 때 회귀분석을 사용하면, 신뢰할만한 결과를 얻기 어렵다. 이에본 연구는 확률계수모형과 위계선형모형을 이용하였다. 분석결과 인구와 건축허가변수가 아파트 가격에 유의한 영향을 미치는 것으로 나타났다. 그러나 중국인의 거래건수변수는 유의하지 않았다. 최근 제주도 부동산시장에서 중국인 거래가 급격히 증가하여 제주도 부동산 가격상승을 유발한다는 우려가 많지만, 실증적으로는 지지되지 않았다. 유의하지 않은 변수들 중에는“아파트의 노후정도”를 나타내는 아파트나이 변수가 있었다. 확률계수모형과 위계선형모형 모두에서 아파트의 노후정도변수는 가격에 영향이 없었다. 이는 아파트가 오래되어 노후해도 가격이 하락하지 않음을 의미한다. 현재 제주시 내 대부분의 아파트들이 노후화 된 상태지만, 새로운 아파트의 공급이 부족하여 노후화된 아파트라 할지라도 수요가 감소하지 않고 있다. 이러한 시장현상을 모형에서 확인할수 있었다. 이러한 결과는 결국 부동산가격이 상승하는 제주도 부동산시장에 새로운 주택공급이 필요하다는 것을 보여준다고 하겠다. This study uses 2012 apartment data from Jeju City analyzed using an Unconditional model, Random coefficient model, and Hierarchical Linear Model (HLM). The results of the unconditional and random coefficient models suggest that apartment price differences exist among the 14 administrative units of Jeju City which constitute separate sub-markets. Further, these results illustrate heterogeneity between apartment sub-markets in Jeju City. In the face of such heterogeneity, it is difficult to obtain reliable results from simple regression analysis, which is why the current study uses a random coefficient model and a Hierarchical Linear Model Analysis reveals that the variables population and building permits exerted a significant influence on apartment prices, however, the variable number of transactions by Chinese nationals was not significant. Recently there has been a sharp rise in Jeju real estate transactions carried out by Chinese nationals, which has given rise to fears that this will lead to rising real estate prices, but this hypothesis is not supported by empirical data. Also the variable Apartment age was not significant. This variable failed to show any influence on apartment prices in the random coefficient model and in the Hierarchical Linear Model. This can be interpreted to mean that even old and dilapidated apartments are not exhibiting decreases in price. Currently most apartments in Jeju City are old and the supply of new apartments is insufficient, so old apartments are still in demand. This market phenomenon is confirmed in the market models. These results show the need for additional housing supply in Jeju as local real estate prices continue to rise.

Models for Estimating Genetic Parameters of Milk Production Traits Using Random Regression Models in Korean Holstein Cattle

Cho, C.I.,Alam, M.,Choi, T.J.,Choy, Y.H.,Choi, J.G.,Lee, S.S.,Cho, K.H. Asian Australasian Association of Animal Productio 2016 Animal Bioscience Vol.29 No.5

The objectives of the study were to estimate genetic parameters for milk production traits of Holstein cattle using random regression models (RRMs), and to compare the goodness of fit of various RRMs with homogeneous and heterogeneous residual variances. A total of 126,980 test-day milk production records of the first parity Holstein cows between 2007 and 2014 from the Dairy Cattle Improvement Center of National Agricultural Cooperative Federation in South Korea were used. These records included milk yield (MILK), fat yield (FAT), protein yield (PROT), and solids-not-fat yield (SNF). The statistical models included random effects of genetic and permanent environments using Legendre polynomials (LP) of the third to fifth order (L3-L5), fixed effects of herd-test day, year-season at calving, and a fixed regression for the test-day record (third to fifth order). The residual variances in the models were either homogeneous (HOM) or heterogeneous (15 classes, HET15; 60 classes, HET60). A total of nine models (3 orders of $polynomials{\times}3$ types of residual variance) including L3-HOM, L3-HET15, L3-HET60, L4-HOM, L4-HET15, L4-HET60, L5-HOM, L5-HET15, and L5-HET60 were compared using Akaike information criteria (AIC) and/or Schwarz Bayesian information criteria (BIC) statistics to identify the model(s) of best fit for their respective traits. The lowest BIC value was observed for the models L5-HET15 (MILK; PROT; SNF) and L4-HET15 (FAT), which fit the best. In general, the BIC values of HET15 models for a particular polynomial order was lower than that of the HET60 model in most cases. This implies that the orders of LP and types of residual variances affect the goodness of models. Also, the heterogeneity of residual variances should be considered for the test-day analysis. The heritability estimates of from the best fitted models ranged from 0.08 to 0.15 for MILK, 0.06 to 0.14 for FAT, 0.08 to 0.12 for PROT, and 0.07 to 0.13 for SNF according to days in milk of first lactation. Genetic variances for studied traits tended to decrease during the earlier stages of lactation, which were followed by increases in the middle and decreases further at the end of lactation. With regards to the fitness of the models and the differential genetic parameters across the lactation stages, we could estimate genetic parameters more accurately from RRMs than from lactation models. Therefore, we suggest using RRMs in place of lactation models to make national dairy cattle genetic evaluations for milk production traits in Korea.

임의회귀 검정일 모형을 이용한 홀스타인 젖소의 1산차 산유형질 및 체세포지수에 대한 유전모수

이득환,조주현,한광진 한국동물자원과학회 2003 한국축산학회지 Vol.45 No.5

The objective of this study was to estimate genetic parameters for test-day milk production and somatic cell score using field data collected by dairy herd improvements program in Korea. Random regression animal models were applied to estimate genetic variances for milk production and somatic cell score. Heritabilities for milk yields, fat percentage, protein percentage, solid-not-fat percentage, and somatic cell score from test day records of 5,796 first lactation Holstein cows were estimated by REML algorithm in single trait random regression test-day animal models. For these analyses, Legendre polynomial covariate function was applied to model the fixed effect of age-season, the additive genetic effect and the permanent environment effect as random. Homogeneous residual variance was assumed to be equal throughout lactation. Heritabilities as a function of time were calculated from curve parameters from univariate analyses. Heritability estimates for milk yields were in range of 0.13 to 0.29 throughout first lactation. Heritability estimates for fat percentage, protein percentage and solid-not-fat percentage were withing 0.09 to 0.11, 0.12 to 0.19 and 0.17 to 0.23, respectively. For somatic cell score, heritabilities were within 0.02 to 0.04. Heritabilities for milk productions and somatic cell score were fluctuated by days in milk with comparing 305d milk production.

기계학습을 이용한 유동가속부식 모델링: 랜덤 포레스트와 비선형 회귀분석과의 비교

이경근 ( Gyeong-geun Lee ),이은희 ( Eun Hee Lee ),김성우 ( Sung-woo Kim ),김경모 ( Kyung-mo Kim ),김동진 ( Dong-jin Kim ) 한국부식방식학회(구 한국부식학회) 2019 Corrosion Science and Technology Vol.18 No.2

Flow-Accelerated Corrosion (FAC) is a phenomenon in which a protective coating on a metal surface is dissolved by a flow of fluid in a metal pipe, leading to continuous wall-thinning. Recently, many countries have developed computer codes to manage FAC in power plants, and the FAC prediction model in these computer codes plays an important role in predictive performance. Herein, the FAC prediction model was developed by applying a machine learning method and the conventional nonlinear regression method. The random forest, a widely used machine learning technique in predictive modeling led to easy calculation of FAC tendency for five input variables: flow rate, temperature, pH, Cr content, and dissolved oxygen concentration. However, the model showed significant errors in some input conditions, and it was difficult to obtain proper regression results without using additional data points. In contrast, nonlinear regression analysis predicted robust estimation even with relatively insufficient data by assuming an empirical equation and the model showed better predictive power when the interaction between DO and pH was considered. The comparative analysis of this study is believed to provide important insights for developing a more sophisticated FAC prediction model.

Random Regression Models Using Legendre Polynomials to Estimate Genetic Parameters for Test-day Milk Protein Yields in Iranian Holstein Dairy Cattle

Naserkheil, Masoumeh,Miraie-Ashtiani, Seyed Reza,Nejati-Javaremi, Ardeshir,Son, Jihyun,Lee, Deukhwan Asian Australasian Association of Animal Productio 2016 Animal Bioscience Vol.29 No.12

The objective of this study was to estimate the genetic parameters of milk protein yields in Iranian Holstein dairy cattle. A total of 1,112,082 test-day milk protein yield records of 167,269 first lactation Holstein cows, calved from 1990 to 2010, were analyzed. Estimates of the variance components, heritability, and genetic correlations for milk protein yields were obtained using a random regression test-day model. Milking times, herd, age of recording, year, and month of recording were included as fixed effects in the model. Additive genetic and permanent environmental random effects for the lactation curve were taken into account by applying orthogonal Legendre polynomials of the fourth order in the model. The lowest and highest additive genetic variances were estimated at the beginning and end of lactation, respectively. Permanent environmental variance was higher at both extremes. Residual variance was lowest at the middle of the lactation and contrarily, heritability increased during this period. Maximum heritability was found during the 12th lactation stage ($0.213{\pm}0.007$). Genetic, permanent, and phenotypic correlations among test-days decreased as the interval between consecutive test-days increased. A relatively large data set was used in this study; therefore, the estimated (co)variance components for random regression coefficients could be used for national genetic evaluation of dairy cattle in Iran.

Prediction of Future Milk Yield with Random Regression Model Using Test-day Records in Holstein Cows

Park, Byoungho,Lee, Deukhwan Asian Australasian Association of Animal Productio 2006 Animal Bioscience Vol.19 No.7

Various random regression models with different order of Legendre polynomials for permanent environmental and genetic effects were constructed to predict future milk yield of Holstein cows in Korea. A total of 257,908 test-day (TD) milk yield records from a total of 28,135 cows belonging to 1,090 herds were considered for estimating (co)variance of the random covariate coefficients using an expectation-maximization REML algorithm in an animal mixed model. The variances did not change much between the models, having different order of Legendre polynomial, but a decreasing trend was observed with increase in the order of Legendre polynomial in the model. The R-squared value of the model increased and the residual variance reduced with the increase in order of Legendre polynomial in the model. Therefore, a model with $5^{th}$ order of Legendre polynomial was considered for predicting future milk yield. For predicting the future milk yield of cows, 132,771 TD records from 28,135 cows were randomly selected from the above data by way of preceding partial TD record, and then future milk yields were estimated using incomplete records from each cow randomly retained. Results suggested that we could predict the next four months milk yield with an error deviation of 4 kg. The correlation of more than 70% between predicted and observed values was estimated for the next four months milk yield. Even using only 3 TD records of some cows, the average milk yield of Korean Holstein cows would be predicted with high accuracy if compared with observed milk yield. Persistency of each cow was estimated which might be useful for selecting the cows with higher persistency. The results of the present study suggested the use of a $5^{th}$ order Legendre polynomial to predict the future milk yield of each cow.