• 지능정보연구
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• 제1권2호
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• pp.103-121
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• 1995

This study proposes a artificial neural network method to predict the time to buy and sell the stocks listed on the Korea Composite Stock Price Index(KOSPI). Four types (NN1, NN2, NN3, NN4) of independent networks were developed to predict KOSPIs up/down direction after four weeks. These networks have a difference only in the length of learning period. NN5 - arithmetic average of four networks outputs - shows an higher accuracy than other network types and Multiple Linear Regression (MLR), and buying and selling simulation using systems outputs produces higher reture than buy-and-hold strategy.

• 한국컴퓨터정보학회:학술대회논문집
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• 한국컴퓨터정보학회 2020년도 제62차 하계학술대회논문집 28권2호
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• pp.139-142
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• 2020

NB-IoT(Narrow Band Internet of Things) is an emerging LPWAN(Low Power Wide Area Network) radio technology. NB-IoT has many advantages like low power, low cost, and high coverage. However low bandwidth and low sampling rates also lead to poor positioning accuracy. This paper proposed a solution to optimize positioning accuracy under the OTDOA(Observed Time Difference of Arrival) approach by utilizing MLR(Multiple Linear Regression) models. Through the MLR model to predict the influence degree of weather(temperature, humidity, light intensity and air pressure) on the arrival time of signal transmission to improve the measurement accuracy. The improvement of measurement accuracy can greatly improve IoT applications based on NB-IoT.

• 분석과학
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• 제28권3호
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• pp.187-195
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• 2015

승화열은 대기 유기 오염물질의 확산에 관련된 환경적인 문제를 해결하거나, 위험한 화학 물질의 위해성을 평가하는 데에 중요한 변수이다. 하지만 실험적으로 승화열을 측정하려면 많은 시간과 비용이 소모 되며, 그 실험자체도 복잡하고 위험하다. 따라서 본 연구에서는 유기화합물의 승화열을 간단하게 예측하는 모델을 개발하기 위하여 정량적 구조-물성 상관관계 연구를 이용하였다. 군기반 전진선택방법을 적용하여 다중선형회귀방법과 서포트 벡터 머신과 같은 학습방법에 적합한 분자표현자들을 선택하도록 하였다. 개별 모델과 복합모델들은 부스트래핑 방법과 y-임의추출법에 의해 내부검증이 되었다. 외부 테스트 데이터의 예측 성능은 적용범위를 고려하므로서 개선되었다. 다중선형회귀모델에 따르면, 승화열은 분자간의 분산력, 수소결합, 정전기적 상호작용, 쌍극자-쌍극자 상호작용과 관련이 있는 것을 나타낼 수 있었다.

• IMMUNE NETWORK
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• 제2권3호
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• pp.133-136
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• 2002

Background: The costimulatory molecule 4-1BB, a member of nerve growth factor receptor/tumor necrosis factor (NGFR/TNFR) super family, is involved in cell survival and death. Methods: In this study, female C57BL/6 ($H-2^b$) mice were used as a recipient, and DBA/2 ($H-2^d$) as a donor to assess a mixed lymphocyte reaction (MLR) and CTL response in vitro, and skin graft survival. IL-2, IFN level was measured by ELISA. Results: Mixed lymphocyte reaction (MLR) analysis showed that 4-1BB-deficient responder cells showed enhanced cellular proliferation over littermate controls. In contrast, IL-2 production was diminished only in 4-1BB knockout cultures. The IFN expression, on the other hand, was comparable between the groups. When female C57BL/6 ($H-2^b$) mice were grafted with the trunk skin of DBA/2 ($H-2^d$) mice, the in vivo tissue destruction of 4-1BB-deficient mice was not distinct from the normal littermates. Conclusion: These data suggest that 4-1BB is critical for the induction of alloreactive responses in vitro but 4-1BB alone could not change the course of skin rejection in vivo.

• 한국환경보건학회지
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• 제41권2호
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• pp.90-101
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• 2015

Objective: This study was conducted to develop a chemical oxygen demand (COD) regression model using water quality monitoring data (January, 2014) obtained from the Han River auto-monitoring stations. Methods: Surface water quality data at 198 sampling stations along the six major areas were assembled and analyzed to determine the spatial distribution and clustering of monitoring stations based on 18 WQPs and regression modeling using selected parameters. Statistical techniques, including combined genetic algorithm-multiple linear regression (GA-MLR), cluster analysis (CA) and principal component analysis (PCA) were used to build a COD model using water quality data. Results: A best GA-MLR model facilitated computing the WQPs for a 5-descriptor COD model with satisfactory statistical results ($r^2=92.64$,$Q{^2}_{LOO}=91.45$,$Q{^2}_{Ext}=88.17$). This approach includes variable selection of the WQPs in order to find the most important factors affecting water quality. Additionally, ordination techniques like PCA and CA were used to classify monitoring stations. The biplot based on the first two principal components (PCs) of the PCA model identified three distinct groups of stations, but also differs with respect to the correlation with WQPs, which enables better interpretation of the water quality characteristics at particular stations as of January 2014. Conclusion: This data analysis procedure appears to provide an efficient means of modelling water quality by interpreting and defining its most essential variables, such as TOC and BOD. The water parameters selected in a COD model as most important in contributing to environmental health and water pollution can be utilized for the application of water quality management strategies. At present, the river is under threat of anthropogenic disturbances during festival periods, especially at upstream areas.

• 한국근적외분광분석학회:학술대회논문집
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• 한국근적외분광분석학회 2001년도 NIR-2001
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• pp.4111-4111
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• 2001

When a drug is prepared in a tablet, the active component represents only a small portion of the dosage form. The other components of the formulation include materials to assist in the dissolution, antioxidants, coloring agents and bulk fillers. The tablets are tested using approved testing methods usually involving separation and subsequent quantification of the active component. Tablets may also be tested by near-Infrared Reflectance spectrometry (NIRS). In the present study, based on NIRS and multivariate calibration methods, a novel and precise method is developed for direct determination of ascorbic acid in vitamin C tablet. Two different tablet formulations were powdered in three different sizes, 63-125 ${\mu}{\textrm}{m}$, and examined. Spectral region of 4750-4950 $cm^{-1}$ / was used and optimized for quantitative operations. Partial least squares (PLS) and multiple linear regression (MLR) methods were performed for this spectral region. The results of optimized PLS and MLR methods showed that reproducibility increase with decreasing grain size and standard error of calibration (SEP) of less than 1% w/w of ascorbic acid and a correlation coefficient of 0.998 can be achieved. The PLS method showed better results than MLR. Seven overdose and underdose samples (prepared in the laboratory to match marketed products) were tested by proposed and iodometric standard methods. A correlation between NIRS predicted ascorbic acid values and iodomet.ic values was calculated ($R^2$=0.9950). Finally, the direct analysis of individual intact tablets in their unit-dose packages (Blistering in aluminum and PVC foils) obtained from market were also carried out and a correlation coefficient of 0.9989 and SEP of 0.931% w/w of ascorbic acid were achieved.

• 한국근적외분광분석학회:학술대회논문집
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• 한국근적외분광분석학회 2001년도 NIR-2001
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• pp.1528-1528
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• 2001

Ginseng cultivated in different country or growing condition has generally different components such as saponin and protein, and it relates to efficacy and action. Protein content assumes by nitrogen content in ginseng radix. Nitrogen content could be determined by chemical analysis such as kjeldahl or extraction methods. However, these methods require long analysis time and result environmental pollution and sample damage. In this work we investigated possibility of non-destructive determination of nitrogen content in ginseng radix using near-infrared spectroscopy. Ginseng radix, root of Panax ginseng C. A. Meyer, was studied. Total 120 samples were used in this study and it was consisted of 6 sample sets, 4, 5 and 6-year-old Korea ginseng and 7, 8 and 9-year-old China ginseng, respectively. Each sample set has 20 sample. Nigrogen content was measured by electronic analysis. NIR reflectance spectra were collected over the 1100 to 2500 nm spectral region with a InfraAlyzer 500C (Bran+Luebbe, Germany) equipped with a halogen lapmp and PbS detector and data were collected every 2 nm data point intervals. The calibration models were carried out by multiple linear regression (MLR) and partial least squares (PLS) analysis using IDAS and SESAME software. Result of electronic analysis, Korean ginseng were different mean value in nitrogen content of China ginseng. Ginseng tend to generally decrease the nitrogen content according as cultivation year is over 6 years. The MLR calibration model with 8 wavelengths using IDAS software accurately predicted nitrogen contents with correlation coefficient (R) and standard error of prediction of 0.985 and 0.855%, respectively. In case of SESAME software, the MLR calibration with 9 wavelength was selected the best calibration, R and SEP were 0.972 and 0.596%, respectively. The PLSR calibration model result in 0.969 of R and 0.630 of RMSEP. This study shows the NIR spectroscopy could be applied to determine the nitrogen content in ginseng radix with high accuracy.

• Toxicological Research
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• 제32권4호
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• pp.337-343
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• 2016

We determined the toxicity of mixtures of ethyl acetate (EA), isopropyl alcohol (IPA), methyl ethyl ketone (MEK), toluene (TOL) and xylene (XYL) with half-maximal effective concentration ($EC_{50}$) values obtained using human hepatocytes cells. According to these data, quantitative property-activity relationships (QPAR) models were successfully proposed to predict the toxicity of mixtures by multiple linear regressions (MLR). The leave-one-out cross validation method was used to find the best subsets of descriptors in the learning methods. Significant differences in physico-chemical properties such as boiling point (BP), specific gravity (SG), Reid vapor pressure (rVP) and flash point (FP) were observed between the single substances and the mixtures. The $EC_{50}$ of the mixture of EA and IPA was significantly lower than that of contained TOL and XYL. The mixture toxicity was related to the mixing ratio of MEK, TOL and XYL (MLR equation $EC_{50}=3.3081-2.5018{\times}TOL-3.2595{\times}XYL-12.6596{\times}MEK{\times}XYL$), as well as to BP, SG, VP and FP (MLR equation $EC_{50}=1.3424+6.2250{\times}FP-7.1198{\times}SG{\times}FP-0.03013{\times}rVP{\times}FP$). These results suggest that QPAR-based models could accurately predict the toxicity of polar and nonpolar mixtures used in rotogravure printing industries.

• Bulletin of the Korean Chemical Society
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• 제35권12호
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• pp.3637-3641
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• 2014

In this study, we analyzed the toxicity of mixtures of dimethylformamide (DMF) and methyl ethyl ketone (MEK) or DMF and toluene (TOL) and predicted their toxicity using quantitative structure-activity relationships (QSAR). A QSAR model for single substances and mixtures was analyzed using multiple linear regression (MLR) by taking into account the statistical parameters between the observed and predicted $EC_{50}$. After preprocessing, the best subsets of descriptors in the learning methods were determined using a 5-fold cross-validation method. Significant differences in physico-chemical properties such as boiling point (BP), specific gravity (SG), Reid vapor pressure (rVP), flash point (FP), low explosion limit (LEL), and octanol/water partition coefficient (Pow) were observed between the single substances and the mixtures. The $EC_{50}$ of the mixture of DMF and TOL was significantly lower than that of DMF. The mixture toxicity was directly related to the mixing ratio of TOL and MEK (MLR $EC_{50}$ equation = $1.76997-1.12249{\times}TOL+1.21045{\times}MEK$), as well as to SG, VP, and LEL (MLR equation $EC_{50}=15.44388-19.84549{\times}SG+0.05091{\times}VP+1.85846{\times}LEL$). These results show that QSAR-based models can be used to quantitatively predict the toxicity of mixtures used in manufacturing industries.

• Advances in Computational Design
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• 제2권3호
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• pp.225-240
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• 2017

This paper presents the application of multiple linear regression (MLR) and artificial neural network (ANN) techniques for developing the models to predict the unconfined compressive strength (UCS) and Brazilian tensile strength (BTS) of the fiber reinforced cement stabilized fly ash mixes. UCS and BTS is a highly nonlinear function of its constituents, thereby, making its modeling and prediction a difficult task. To establish relationship between the independent and dependent variables, a computational technique like ANN is employed which provides an efficient and easy approach to model the complex and nonlinear relationship. The data generated in the laboratory through systematic experimental programme for evaluating UCS and BTS of fiber reinforced cement fly ash mixes with respect to 7, 14 and 28 days' curing is used for development of the MLR and ANN model. The data used in the models is arranged in the format of four input parameters that cover the contents of cement and fibers along with maximum dry density (MDD) and optimum moisture contents (OMC), respectively and one dependent variable as unconfined compressive as well as Brazilian tensile strength. ANN models are trained and tested for various combinations of input and output data sets. Performance of networks is checked with the statistical error criteria of correlation coefficient (R), mean square error (MSE) and mean absolute error (MAE). It is observed that the ANN model predicts both, the unconfined compressive and Brazilian tensile, strength quite well in the form of R, RMSE and MAE. This study shows that as an alternative to classical modeling techniques, ANN approach can be used accurately for predicting the unconfined compressive strength and Brazilian tensile strength of fiber reinforced cement stabilized fly ash mixes.