• Transactions of Materials Processing
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• v.21 no.4
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• pp.246-251
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• 2012

The mechanism for mechano-luminescence(ML) in SAO phosphor was investigated quantitatively by measuring the emission intensity under three different tensile conditions. It was found that the ML of SAO was strongly dependent on the dynamic loading rate rather than by the applied load itself. The mechano-luminescent emission in SAO was evaluated based on the trap-releasing process. It was found that the shape of the ML curve in the transient regime obtained from the rate equation has good agreement with the experimental data.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1248-1248
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• 2001

Mastitis is a major problem for the global dairy industry and causes substantial economic losses from decreasing milk production and considerable compositional changes in milk, reducing milk quality. The potential of near infrared (NIR) spectroscopy in the region from 1100 to 2500nm and chemometric method for classification to detect milk from mastitic cows was investigated. A total of 189 milk samples from 7 Holstein cows were collected for 27 days, consecutively, and analyzed for somatic cells (SCC). Three of the cows were healthy, and the rest had mastitis periods during the experiment. NIR transflectance milk spectra were obtained by the InfraAlyzer 500 spectrophotometer in the spectral range from 1100 to 2500nm. All samples were divided into calibration set and test set. Class variable was assigned for each sample as follow: healthy (class 1) and mastitic (class 2), based on milk SCC content. The classification of the samples was performed using soft independent modeling of class analogy (SIMCA) and different spectral data pretreatment. Two concentration of SCC - 200 000 cells/ml and 300 000 cells/ml, respectively, were used as thresholds fer separation of healthy and mastitis cows. The best detection accuracy was found for models, obtained using 200 000 cells/ml as threshold and smoothed absorbance data - 98.41% from samples in the calibration set and 87.30% from the samples in the independent test set were correctly classified. SIMCA results for classes, based on 300 000 cells/ml threshold, showed a little lower accuracy of classification. The analysis of changes in the loading of first PC factor for group of healthy milk and group of mastitic milk showed, that separation between classes was indirect and based on influence of mastitis on the milk components. The accuracy of mastitis detection by SIMCA method, based on NIR spectra of milk would allow health screening of cows and differentiation between healthy and mastitic milk samples. Having SIMCA models, mastitis detection would be possible by using only DIR spectra of milk, without any other analyses.

• Communications for Statistical Applications and Methods
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• v.25 no.6
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• pp.633-645
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• 2018

Gaussian error distributions are a common choice in traditional regression models for the maximum likelihood (ML) method. However, this distributional assumption is often suspicious especially when the error distribution is skewed or has heavy tails. In both cases, the ML method under normality could break down or lose efficiency. In this paper, we consider the log-concave and Gaussian scale mixture distributions for error distributions. For the log-concave errors, we propose to use a smoothed maximum likelihood estimator for stable and faster computation. Based on this, we perform comparative simulation studies to see the performance of coefficient estimates under normal, Gaussian scale mixture, and log-concave errors. In addition, we also consider real data analysis using Stack loss plant data and Korean labor and income panel data.

• Journal of Pharmaceutical Investigation
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• v.31 no.4
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• pp.209-216
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• 2001

The purpose of the present study was to investigate the hepatic uptake and biliary excretion of l-anilino-8-naphthalene sulfonate (ANS) in vivo. The plasma concentration and liver concentration of ANS were determined after its i.v. bolus administration at a dose of $30\;{\mu}mol/kg$ in rats. The hepatic uptake clearance $(CL_{uptake})$ of ANS was 0.1 ml/min/g liver. On the basis of the unbound concentration of ANS, the permeability-surface area product $(PS_{influx})$ was calculated to be l0.4 ml/min/g liver, being comparable of in vitro data. On the other hand, we determined the plasma concentration, liver concentration and biliary excretion rate of ANS at steady-state after its i. v. infusion $(0.2-1.6\;{\mu}mol/min/kg)$ in rats. The excretion clearance $(CL_{excretion})$ of ANS showed Michaelis-Menten kinetics with increasing the infusion rate. The permeability-surface area product $(PS_{excretion})$ based on the unbound concentration in the liver was calculated to be 0.0165 ml/min/g liver, which is negligible compared with the intrinsic clearance $(CL_{int}=3.3\;ml/min/g\;liver)$ by rat liver microsomes. The sequestration process of ANS, therefore, was considered to be mainly due to the metabolic process in the liver $(PS_{seq}{\risingdotseq}CL_{int})$. Furthermore, $PS_{efflux}$ value calculated from $PS_{influx}$ and $PS_{seq}$ was 4.4 ml/min/g liver, which was comparable of in vitro data. In conclusion, in vivo parameters such as $PS_{influx}$, $PS_{efflux}$ and $PS_{seq}$ in the present study showed good in vivo-in vitro relationship. Thus, the kinetic analysis method proposed in the present study would be useful to analyze the hepatic transport of drugs in vivo.

• Journal of Korea Society of Industrial Information Systems
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• v.25 no.2
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• pp.129-136
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• 2020

Recognizing finger movements have been used as a intuitive way of human-computer interaction. In this study, we implement an wearable device for finger motion recognition and evaluate the accuracy of several ML (Machine learning) techniques. Not only HMM (Hidden markov model) and DTW (Dynamic time warping) techniques that have been traditionally used as time series data analysis, but also NN (Neural network) technique are applied to compare and analyze the accuracy of each technique. In order to minimize the computational requirement, we also apply the pre-processing to each ML techniques. Our extensive evaluations demonstrate that the NN-based gesture recognition system achieves 99.1% recognition accuracy while the HMM and DTW achieve 96.6% and 95.9% recognition accuracy, respectively.

• Smart Structures and Systems
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• v.31 no.5
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• pp.485-500
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• 2023

Machine learning-based structural health monitoring (ML-based SHM) methods are researched extensively in the recent decade due to the availability of advanced information and sensing technology. ML methods are well-known for their pattern recognition capability for complex problems. However, the main obstacle of ML-based SHM is that it often requires pre-collected historical data for model training. In most actual scenarios, damage presence can be detected using the unsupervised learning method through anomaly detection, but to further identify the damage types would require prior knowledge or historical events as references. This creates the cold-start problem, especially for new and unobserved structures. Modal-based methods identify damages based on the changes in the structural global properties but often require dense measurements for accurate results. Therefore, a two-stage hybrid modal-machine learning damage detection scheme is proposed. The first stage detects damage presence using Principal Component Analysis-Frequency Response Function (PCA-FRF) in an unsupervised manner, whereas the second stage further identifies the damage. To solve the cold-start problem, mode shape assessment using the first mode is initiated when no trained model is available yet in the second stage. The damage identified by the modal-based method would be stored for future training. This work highlights the performance of the scheme in alleviating the cold-start issue as it transitions through different phases, starting from zero damage sample available. Results showed that single and multiple damages can be identified at an acceptable accuracy level even when training samples are limited.

• KEPCO Journal on Electric Power and Energy
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• v.7 no.1
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• pp.171-177
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• 2021

In terms of distribution planning, accurate electric load prediction is one of the most important factors. The future load prediction has manually been performed by calculating the maximum electric load considering loads transfer/switching and multiplying it with the load increase rate. In here, the risk of human error is inherent and thus an automated maximum electric load forecasting system is required. Although there are many existing methods and techniques to predict future electric loads, such as regression analysis, many of them have limitations in reflecting the nonlinear characteristics of the electric load and the complexity due to Photovoltaics (PVs), Electric Vehicles (EVs), and etc. This study, therefore, proposes a method of predicting future electric loads on distribution lines by using Machine Learning (ML) method that can reflect the characteristics of these nonlinearities. In addition, predictive models were developed based on actual data collected at KEPCO's existing distribution lines and the adequacy of developed models was verified as well. Also, as the distribution planning has a direct bearing on the investment, and amount of investment has a direct bearing on the maximum electric load, various baseline such as maximum, lowest, median value that can assesses the adequacy and accuracy of proposed ML based electric load prediction methods were suggested.

• Journal of dental hygiene science
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• v.17 no.3
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• pp.233-241
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• 2017

This study identified an effective control method for periodontitis by investigating the association between blood levels of vitamin D and periodontitis in Korean elderly based on raw data from the fifth Korea National Health & Nutrition Examination Survey of 2010 (KNHANES). In this study, 1,021 adults over 65 years of age were evaluated based on data from the KNHANES. Periodontal disease was assessed using community periodontal index (CPI), with CPI codes ${\geq}3$ defined as periodontitis. Blood levels of vitamin D were measured from blood samples and divided into four groups (first quartile: ${\leq}13.23ng/ml$, second quartile: 13.24~16.95 ng/ml, third quartile: 16.96~21.58 ng/ml), and fourth quartile >21.59 ng/ml). Using multiple logistic regression analyses, the variables were adjusted for general characteristics, oral health-related characteristics, health-related characteristics, and bone mineral density. The statistical analysis was performed using the SAS (ver. 9.2). The results of this study are as follows: the prevalence of periodontitis was 42.6% in Korean elderly. After adjusting for general, oral health-related, and health-related, the risk of periodontitis in the first quartile group was 1.74 times (95% confidence interval [CI], 1.02~2.98) higher than that of the fourth quartile group (p=0.041). After adjusting for general, oral health-related, and health-related characteristics as well as bone mineral density, the risk of periodontitis in the first quartile group was 1.73 times (95% CI, 1.02~2.96) higher than that of the four quartile group (p=0.042). There was a significant relationship between blood vitamin D level and periodontitis in Korean elderly. For the prevention of periodontitis, factors related to vitamin D should be considered along with other risk factors.

• Proceedings of the KSR Conference
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• 2007.11a
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• pp.629-632
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• 2007

Communications Based Train Control(CBTC) is a continuous, automatic train control system utilizing high-resolution, train location determination independent of track circuits; continuous, high capacity, bi-directional train-to-wayside data communications; and trainborne and wayside processors capable of implementing vital functions. In this paper, we present a basic design of CBTC system based on ERTMS/ETCS specifications. The basic design is composed of system requirements analysis, system functional analysis, system architectural design, sub-system requirements analysis, sub-system functional analysis. Each deliverable has been produced according to a development process based on UML/SysML.

• Journal of the Korean Association of Oral and Maxillofacial Surgeons
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• v.49 no.3
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• pp.135-141
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• 2023

Objectives: This study aimed to develop and validate machine learning (ML) models using H₂O-AutoML, an automated ML program, for predicting medication-related osteonecrosis of the jaw (MRONJ) in patients with osteoporosis undergoing tooth extraction or implantation. Patients and Methods: We conducted a retrospective chart review of 340 patients who visited Dankook University Dental Hospital between January 2019 and June 2022 who met the following inclusion criteria: female, age ≥55 years, osteoporosis treated with antiresorptive therapy, and recent dental extraction or implantation. We considered medication administration and duration, demographics, and systemic factors (age and medical history). Local factors, such as surgical method, number of operated teeth, and operation area, were also included. Six algorithms were used to generate the MRONJ prediction model. Results: Gradient boosting demonstrated the best diagnostic accuracy, with an area under the receiver operating characteristic curve (AUC) of 0.8283. Validation with the test dataset yielded a stable AUC of 0.7526. Variable importance analysis identified duration of medication as the most important variable, followed by age, number of teeth operated, and operation site. Conclusion: ML models can help predict MRONJ occurrence in patients with osteoporosis undergoing tooth extraction or implantation based on questionnaire data acquired at the first visit.