• 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%.

• Proceedings of the Plant Resources Society of Korea Conference
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• 2020.12a
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• pp.53-53
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• 2020

본 실험은 다양한 생리활성이 보고되어 약용으로 널리 쓰이는 개똥쑥(Artemisia annua L.) 종자의 온도에 따른 발아 특성을 조사하기 위해 진행하였다. 미세온도구배 발아기를 이용하여 낮과 밤의 온도를 각각 5 ~ 35℃ 범위에서 낮과 밤의 시간을 12시간으로 고정하고, 낮 온도가 밤 온도보다 크거나 같은 조건을 설정하여 총 27개의 온도조건으로 개똥쑥 종자의 최종 발아율 및 발아율과의 관계를 분석하였다. 실험결과, 개똥쑥 종자는 실험에 사용한 모든 온도조건에서 발아가 가능한 것으로 나타났으며, 25/10℃(낮/밤) 조건에서 90%로 가장 높게 조사되었다. 또한, 발아율 조사결과를 통해 일평균온도뿐만 아니라 일교차온도도 발아율에 영향을 미치는 것으로 판단되어 일평균온도와 일교차온도로 나누어 발아율과의 관계를 분석하였다. 온도조건과 발아율과의 연속적인 발아특성을 분석하기 위해 다중회귀분석과 비선형 회귀분석을 이용하여 온도 조건과 상대적 발아율의 관계를 수식으로 표현하였다. 일평균온도를 기준으로 발아율과의 관계를 분석한 결과, 5 ~ 35℃의 모든 일평균 온도범위에서 유의성이 나타났으며 5, 7.5, 32.5, 35℃는 상대적인 음의 영향력을, 나머지 조건에서는 상대적인 양의 영향력을 가진 것으로 분석되었다. 일교차온도를 기준으로 발아율과의 관계를 분석한 결과, 0 ~ 25℃의 모든 일교차 온도범위에서 양의 영향력을 가진 것으로 분석되었다. 일평균온도는 19.3℃에 가까울수록, 일교차온도는 14.9℃에 가까울수록 발아에 대한 영향력이 큰 것으로 조사되었다. 각 수식을 통해 도출된 수치화된 온도에 따른 개똥쑥 종자의 일일누적 온도 영향력을 temperature score(TS)로 설정하였다. 본 연구에서 도출된 수식을 통해 누적 TS를 계산한 결과, 14.9 TS가 누적되었을 때 발아율이 85% 이상으로 나타날 것으로 예측되었다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.05a
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• pp.245-251
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• 2002

본 논문에서는 상대적으로 큰 퍼지 엔트로피를 갖는 입력-출력 데이터 집단에 다중 회귀 분석을 적용하여 다차원 평면 클러스터를 생성하고, 이 클러스터를 새로운 퍼지 모델의 규칙으로 추가한 후 퍼지 모델 파라미터의 개략 동조와 정밀 동조를 수행하는 자기구성 퍼지 모델링을 제안한다. Weighted recursive least squared 알고리즘과 fuzzy C-regression model 클러스터링에 의해 퍼지 모델의 파라미터를 개략적으로 동조한 후 gradient descent 알고리즘에 의해 파라미터를 정밀 동조하면서 감수분열 유전 알고리즘을 이용하여 최적의 학습률을 탐색한다. 그리고 자기 구성 퍼지 모델링 기법을 이용하여 Box-Jenkins의 가스로 데이터, 다변수비선형 정적 함수의 데이터와 하수 처리 활성오니 공정의 모델링을 수행하고, 기존의 방법에 의한 모델링 결과와 비교하여 그 성능을 입증한다.

• Proceedings of the Korean Printing Society Conference
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• 2000.12a
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• pp.0.2-0
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• 2000

Most important step for the color matching in scanner is the color coordinate transformation from the scanner RGB space to device independent uniform color space. A variety of color calibration technologies have been developed for input device. Linear or nonlinear matrices have been conveniently applied to correct the color filter\`s mismatch with color matching function in scanners. The color matching accuracy is expected to be further improved when the nonlinear matrices are optimized into subdivided smaller color spaces than in single matrix of the entire color space. This article proposed the scanner calibration method using subspace division regression analysis and it were compared with conventional method.

• Journal of the Korean Graphic Arts Communication Society
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• v.19 no.1
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• pp.4-16
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• 2001

Most important step for the color matching in scanner is the color coordinate transformation from the scanner RGB space to device independent uniform color space. A variety of color calibration technologies have been developed for input device. Linear or nonlinear matrices have been conveniently applied to correct the color filter's mismatch with color matching function in scanners. The color matching accuracy is expected to be further improved when the nonlinear matrices are optimized into subdivided smaller color spaces than in single matrix of the entire color space. This article proposed the scanner calibration method using subspace division regression analysis and it were compared with conventional method.

• Journal of the Korea Institute of Military Science and Technology
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• v.23 no.4
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• pp.426-434
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• 2020

This paper studies the impact of the maintenance time of anti-ship missile Harpoon on operational availability with real field data. The Harpoon maintenance simulation model is developed as a testbed for identifying the optimal inventory levels on operational availability. Using multiple linear regression analysis and integer programming, the optimal inventory levels of essential assemblies are suggested. Finally, the result of sensitivity analysis shows the quantitative impact of maintenance time on operational availability and inventory costs. The authors believe that this quantitative analysis can support policy decisions to decrease maintenance time of missiles.

• Analytical Science and Technology
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• v.24 no.6
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• pp.533-543
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• 2011

The hERG (human ether-a-go-go related gene) ion channel is a main factor for cardiac repolarization, and the blockade of this channel could induce arrhythmia and sudden death. Therefore, potential hERG ion channel inhibitors are now a primary concern in the drug discovery process, and lots of efforts are focused on the minimizing the cardiotoxic side effect. In this study, $IC_{50}$ data of 202 organic compounds in HEK (human embryonic kidney) cell from literatures were used to develop predictive 2D-QSAR model. Multiple linear regression (MLR), Support Vector Machine (SVM), and artificial neural network (ANN) were utilized to predict inhibition concentration of hERG ion channel as machine learning methods. Population based-forward selection method with cross-validation procedure was combined with each learning method and used to select best subset descriptors for each learning algorithm. The best model was ANN model based on 14 descriptors ($R^2_{CV}$=0.617, RMSECV=0.762, MAECV=0.583) and the MLR model could describe the structural characteristics of inhibitors and interaction with hERG receptors. The validation of QSAR models was evaluated through the 5-fold cross-validation and Y-scrambling test.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.1-25
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• 2022

After the first Covid-19 confirmed case occurred in Korea in January 2020, interest in personal transportation such as public bicycles not public transportation such as buses and subways, increased. The demand for 'Ddareungi', a public bicycle operated by the Seoul Metropolitan Government, has also increased. In this study, a demand prediction model of a GRU(Gated Recurrent Unit) was presented based on the rental history of public bicycles by time zone(2019~2021) in Seoul. The usefulness of the GRU method presented in this study was verified based on the rental history of Around Exit 1 of Yeouido, Yeongdengpo-gu, Seoul. In particular, it was compared and analyzed with multiple linear regression models and recurrent neural network models under the same conditions. In addition, when developing the model, in addition to weather factors, the Seoul living population was used as a variable and verified. MAE and RMSE were used as performance indicators for the model, and through this, the usefulness of the GRU model proposed in this study was presented. As a result of this study, the proposed GRU model showed higher prediction accuracy than the traditional multi-linear regression model and the LSTM model and Conv-LSTM model, which have recently been in the spotlight. Also the GRU model was faster than the LSTM model and the Conv-LSTM model. Through this study, it will be possible to help solve the problem of relocation in the future by predicting the demand for public bicycles in Seoul more quickly and accurately.

• Tuberculosis and Respiratory Diseases
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• v.43 no.5
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• pp.755-762
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• 1996

Background : Continuous positive airway pressure(CPAP) is doubtlessly using as a medical treatment of choice for patients with obstructive sleep apnea (OSA) syndrome. CPAP is effective in OSA patients as a physical "pneumatic pressure splint" mechanism. We have done this study for two purposes, first to seek for the factors to determine the optimal CPAP titer, second to predict the minimal CPAP titer using the determined factors. Methods: We studied a 72 OSA patients who were treated with CPAP. All of them were studied by using a two nights polysomnographic rests in hospital. We compared the patients requiring CPAP over $10cmH_2O$ with those who required CPAP under 5cm $H_2O$ to determine the factors affecting the minimal CPAP titer. Results : The high CPAP group is characterized by a significantly higher body mass index(BMI), apnea index(AI) and apnea and hyponea index(AHI) and significantly lower lowest $SaO_2$. Regression analysis using the optimal four variables resulted in the following prediction equation for CPAP titer. CPAPtiter=8.382 + 0.064 ${\times}$ BMI + 0.077 ${\times}$ AI - 0.004 ${\times}$ AHI - 0.077 ${\times}$ lowest $SaO_2$ When this regression equation was applied to the 72 patients, the mean CPAP titer as predicted by the above equation was $7.80{\pm}2.96$ mmHg. Compared this value with actually determined CPAPtiter, $7.93{\pm}4.00$mmHg, there was no significant difference between the two values. Conclusion: Obesity, apnea severity and lowest Sa02 were strongly correlated with CPAP titer. Linear regression equation for CPAP titer using these indices predicted very closely the actually measured values in the sleep laboratory.

• Journal of Korean Society of Transportation
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• v.24 no.7 s.93
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• pp.81-90
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• 2006

It is important to clarify the relationship between traffic accidents and various influencing factors in order to reduce the number of traffic accidents. This study developed a traffic accident frequency prediction model using by multi-linear regression and qualification theories which are commonly applied in the field of traffic safety to verify the influences of various factors into the traffic accident frequency The data were collected on the Korean National Highway 17 which shows the highest accident frequencies and fatality rates in Chonbuk province. In order to minimize the uncertainty of the data, the fuzzy theory and neural network theory were applied. The neural network theory can provide fair learning performance by modeling the human neural system mathematically. Tn conclusion, this study focused on the practicability of the fuzzy reasoning theory and the neural network theory for traffic safety analysis.