• Tuberculosis and Respiratory Diseases
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• v.53 no.6
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• pp.619-634
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• 2002

Background : Many inflammatory mediators and collagenases are involved in the pathogenesis of acute lung injury (ALI) and acute respiratory distress syndrome (ARDS). The increase of matrix metalloproteinase-9 (MMP-9, gelatinase-B) produced mainly by inflammatory cells was reported in many ALI models and connective tissue cells. In this study, the expression of MMP-9 in ventilator-induced lung injury (VILI) model and the effects of matrix metalloproteinase inhibitor (MMPI) on VILI were investigated. Methods : Eighteen Sprague-Dawley rats were divided into three groups: low tidal Volume (LVT, 7mL/Kg tidal volume, 3 $cmH_2O$ PEEP, 40/min), high tidal volume (HVT, 30mL/Kg tidal volume, no PEEP, 40/min) and high tidal volume with MMPI (HVT+MMPI) groups. Mechanical ventilation was performed in room air for 2 hours. The 20 mg/Kg of CMT-3 (chemically modified tetracycline-3, 6-demethyl 6-deoxy 4-dedimethylamino tetracycline) was gavaged as MMPI from three days before mechanical ventilation. The degree of lung injury was measured with wet-to-dry weight ratio and acute lung injury score. Expression of MMP-9 was studied by immunohistochemical stain with a mouse monoclonal anti-rat MMP-9 $IgG_1$. Results : In the LVT, HVT and HVT+MMPI groups, the wet-to-dry weight ratio was $4.70{\pm}0.14$, $6.82{\pm}1.28$ and $4.92{\pm}0.98$, respectively. In the HVT group, the ratio was significantly higher than other groups (p<0.05). Acute lung injury score measured by five-point scale was $3.25{\pm}1.17$, $12.83{\pm}1.17$ and $4.67{\pm}0.52$, respectively. The HVT group was significantly damaged by VILI and MMPI protects injuries by mechanical ventilation (p<0.05). Expression of MMP-9 measured by four-point scale was $3.33{\pm}2.07$, $12.17{\pm}2.79$ and $3.60{\pm}1.95$, respectively, which were significantly higher in the HVT group (p<0.05). Conclusion : VILI increases significantly the expression of MMP-9 and MMPI prevents lung injury induced by mechanical ventilation through the inhibition of MMP-9.

• Journal of the Korea Safety Management & Science
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• v.15 no.2
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• pp.243-253
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• 2013

This research paper introduces the application and implementation of medical decision metrics that classifies medical decision-making into four different metrics using statistical diagnostic tools, such as confusion matrix, normal distribution, Bayesian prediction and Receiver Operating Curve(ROC). In this study, the metrics are developed based on cross-section study, cohort study and case-control study done by systematic literature review and reformulated the structure of type I error, type II error, confidence level and power of detection. The study proposed implementation strategies for 10 quality improvement activities via 14 medical decision metrics which consider specificity and sensitivity in terms of ${\alpha}$ and ${\beta}$. Examples of ROC implication are depicted in this paper with a useful guidelines to implement a continuous quality improvement, not only in a variable acceptance sampling in Quality Control(QC) but also in a supplier grading score chart in Supplier Chain Management(SCM) quality. This research paper is the first to apply and implement medical decision-making tools as quality improvement activities. These proposed models will help quality practitioners to enhance the process and product quality level.

• Journal of Navigation and Port Research
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• v.42 no.2
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• pp.107-116
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• 2018

Planning strategies to achieve higher competitiveness of ports are becoming increasingly important in business environment. Therefore, strategic competitive position and efficiency analysis needs to be performed to increase ports' effectiveness and competitiveness. This matches with one of targets of new concept e-Navigation to increase the agility and efficiency of ports. The purpose of this study was to apply Boston Consulting Group matrix to analyze competitive positioning of major ports in Korea and China in term of several main cargo types and then use a combination of Data Envelopment Analysis and Principal Component Analysis model to calculate efficiencies. Results show that, at the moment, Chinese ports are still on the top with high position and efficiency score for the representative-Shanghai port. However, result also points out that except container type, Korean ports have chance to compete in other cargo types. Moreover, Gwangyang port is regarded as efficient. It has better position time. It is believed that Gwangyang port together with Busan port can compete with Chinese port in the near future.

• Journal of Information Technology Applications and Management
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• v.29 no.3
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• pp.43-55
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• 2022

In this study, with the goal of developing a deep learning-based product recommendation model for effective matching of influencers and products, a deep learning model with a collaborative filtering model combined with generalized matrix decomposition(GMF), a collaborative filtering model based on multi-layer perceptron (MLP), and neural collaborative filtering and generalized matrix Factorization (NeuMF), a hybrid model combining GMP and MLP was developed and tested. In particular, we utilize one-class problem free boosting (OCF-B) method to solve the one-class problem that occurs when training is performed only on positive cases using implicit feedback in the deep learning-based collaborative filtering recommendation model. In relation to model selection based on overall experimental results, the MLP model showed highest performance with weighted average precision, weighted average recall, and f1 score were 0.85 in the model (n=3,000, term=15). This study is meaningful in practice as it attempted to commercialize a deep learning-based recommendation system where influencer's promotion data is being accumulated, pactical personalized recommendation service is not yet commercially applied yet.

• Journal of Information Processing Systems
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• v.14 no.5
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• pp.1114-1135
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• 2018

A multiple classification system based on a new boosting technique has been approached utilizing different biometric traits, that is, color face, iris and eye along with fingerprints of right and left hands, handwriting, palm-print, gait (silhouettes) and wrist-vein for person authentication. The images of different biometric traits were taken from different standard databases such as FEI, UTIRIS, CASIA, IAM and CIE. This system is comprised of three different super-classifiers to individually perform person identification. The individual classifiers corresponding to each super-classifier in their turn identify different biometric features and their conclusions are integrated together in their respective super-classifiers. The decisions from individual super-classifiers are integrated together through a mega-super-classifier to perform the final conclusion using programming based boosting. The mega-super-classifier system using different super-classifiers in a compact form is more reliable than single classifier or even single super-classifier system. The system has been evaluated with accuracy, precision, recall and F-score metrics through holdout method and confusion matrix for each of the single classifiers, super-classifiers and finally the mega-super-classifier. The different performance evaluations are appreciable. Also the learning and the recognition time is fairly reasonable. Thereby making the system is efficient and effective.

• Journal of the Microelectronics and Packaging Society
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• v.31 no.2
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• pp.45-53
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• 2024

The maintenance of semiconductor equipment is crucial for the continuous growth of the semiconductor market. System management is imperative given the anticipated increase in the capacity and complexity of industrial equipment. Ensuring optimal operation of manufacturing processes is essential to maintaining a steady supply of numerous parts. Particularly, monitoring the status of substrate transfer robots, which play a central role in these processes, is crucial. Diagnosing failures of their major components is vital for preventive maintenance. Fault diagnosis methods can be broadly categorized into physics-based and data-driven approaches. This study focuses on data-driven fault diagnosis methods due to the limitations of physics-based approaches. We propose a methodology for data acquisition and preprocessing for robot fault diagnosis. Data is gathered from vibration sensors, and the data preprocessing method is applied to the vibration signals. Subsequently, the dataset is trained using Gradient Tree-based XGBoost machine learning classification algorithms. The effectiveness of the proposed model is validated through performance evaluation metrics, including accuracy, F1 score, and confusion matrix. The XGBoost classifiers achieve an accuracy of approximately 92.76% and an equivalent F1 score. ROC curves indicate exceptional performance in class discrimination, with 100% discrimination for the normal class and 98% discrimination for abnormal classes.

• The Journal of Society for e-Business Studies
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• v.24 no.1
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• pp.105-120
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• 2019

The World Health Organization (WHO) and other government agencies aroundthe world have warned against antibiotic-resistant bacteria due to abuse of antibiotics and are strengthening their care and monitoring to prevent infection. However, it is highly necessary to develop an expeditious and accurate prediction and estimating method for preemptive measures. Because it takes several days to cultivate the infecting bacteria to identify the infection, quarantine and contact are not effective to prevent spread of infection. In this study, the disease diagnosis and antibiotic prescriptions included in Electronic Health Records were embedded through neural embedding model and matrix factorization, and deep learning based classification predictive model was proposed. The f1-score of the deep learning model increased from 0.525 to 0.617when embedding information on disease and antibiotics, which are the main causes of antibiotic resistance, added to the patient's basic information and hospital use information. And deep learning model outperformed the traditional machine hospital use information. And deep learning model outperformed the traditional machine learning models.As a result of analyzing the characteristics of antibiotic resistant patients, resistant patients were more likely to use antibiotics in J01 than nonresistant patients who were diagnosed with the same diseases and were prescribed 6.3 times more than DDD.

• Journal of Internet Computing and Services
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• v.22 no.2
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• pp.29-39
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• 2021

As damages to individuals, private sectors, and businesses increase due to newly occurring cyber attacks, the underlying network security problem has emerged as a major problem in computer systems. Therefore, NIDS using machine learning and deep learning is being studied to improve the limitations that occur in the existing Network Intrusion Detection System. In this study, a deep learning-based NIDS model study is conducted using the Convolution Neural Network (CNN) algorithm. For the image classification-based CNN algorithm learning, a discrete algorithm for continuity variables was added in the preprocessing stage used previously, and the predicted variables were expressed in a linear relationship and converted into easy-to-interpret data. Finally, the network packet processed through the above process is mapped to a square matrix structure and converted into a pixel image. For the performance evaluation of the proposed model, NSL-KDD, a representative network packet data, was used, and accuracy, precision, recall, and f1-score were used as performance indicators. As a result of the experiment, the proposed model showed the highest performance with an accuracy of 85%, and the harmonic mean (F1-Score) of the R2L class with a small number of training samples was 71%, showing very good performance compared to other models.

• Journal of the Korean Data and Information Science Society
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• v.21 no.3
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• pp.387-395
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• 2010

It is important to identify the interaction of genes about human disease and characteristic value. Many studies as like logistic analysis, have associated being pursued, but, previous methods did not consider the sub-group of the genotypes. So, QTL interactions and the GMM (genotype matrix mapping) have been developed. In this study, we detect the superior genotype combination to have an impact on economic traits of Korean cattle based on the study over GMM method. Thus, we identified interaction effects of single nucleotide polymorphisms (SNPs) responsible for average daily gain(ADG), marbling score (MS), carcass cold weight (CWT), longissimus muscle dorsiarea (LMA) using GMM method. In addition, we examine significance of the major genotype combination selected by implementing permutation test of the F-measure which was not obtained by Sachiko et al.

• Journal of Oral Medicine and Pain
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• v.44 no.4
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• pp.160-168
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• 2019

Purpose: To search the salivary factors that objectively indicate an pain in myalgia patients with temporomandibular joint disorder (TMD) and determine the possibility of the factors as pain-biomarkers. Methods: Participants consisted of pain-free 15 persons (male 7, female 8, mean age±standard deviation (SD); 26.8±16.04 years) and 45 myalgia patients with TMD (male 21, female 24, mean age±SD; 27.98±13.01 years). They were divided into a pain-free group (numerical rating scale [NRS] score 0), a mild pain group (NRS 1-4), a moderate pain group (NRS 5-6), and a severe pain group (NRS 7-10) and members of all groups were age, sex matched. Interleukin-8 (IL-8) and matrix metalloprotease 9 (MMP-9) were selected as pain biomarkers, by searching the Gene Expression Omnibus database and analyzing pain-related genes. Enzyme-linked immunosorbent assays were used to measure the concentration of IL-8 and MMP-9 in the patients' saliva. Results: IL-8 and MMP-9 levels were statistically significantly higher in pain groups than in the pain-free group. Greater differences were observed in patients with acute pain (with painful duration under 3 months) than in the control group and in female patients than in male. Conclusions: Salivary IL-8 and MMP-9 may play a role as biomarkers of myalgia in patients with TMD.