• 치위생과학회지
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• 제24권1호
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• pp.22-28
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• 2024

Background: Particulate matter (PM) has been extensively observed due to its negative association with human health. Previous research revealed the possible negative effect of air pollutant exposure on oral health. However, the predictive model between air pollutant exposure and the prevalence of periodontitis has not been observed yet. Therefore, this study aims to propose a predictive model for the number of patients with periodontitis exposed to PM and atmospheric factors in South Korea using deep learning. Methods: This study is a retrospective cohort study utilizing secondary data from the Korean Statistical Information Service and the Health Insurance Review and Assessment database for air pollution and the number of patients with periodontitis, respectively. Data from 2015 to 2022 were collected and consolidated every month, organized by region. Following data matching and management, the deep neural networks (DNN) model was applied, and the mean absolute percentage error (MAPE) value was calculated to ensure the accuracy of the model. Results: As we evaluated the DNN model with MAPE, the multivariate model of air pollution including exposure to PM_2.5, PM₁₀, and other atmospheric factors predict approximately 85% of the number of patients with periodontitis. The MAPE value ranged from 12.85 to 17.10 (mean±standard deviation=14.12±1.30), indicating a commendable level of accuracy. Conclusion: In this study, the predictive model for the number of patients with periodontitis is developed based on air pollution, including exposure to PM_2.5, PM₁₀, and other atmospheric factors. Additionally, various relevant factors are incorporated into the developed predictive model to elucidate specific causal relationships. It is anticipated that future research will lead to the development of a more accurate model for predicting the number of patients with periodontitis.

• 한국산업정보학회논문지
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• 제9권2호
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• pp.1-8
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• 2004

본 논문은 누적노출모형에서 수명시간이 지수분포를 따르고 서로 독립일 때 쿨백-라이블러 정보함수를 이용하여 단계 스트레스 가속수명시험으로부터 얻은 자료로부터 모수의 추정량을 제안하고, 단계 스트레스 가속수명시험의 정상조건에서 편의와 평균제곱오차 관점에서 모의실험을 통하여 Vasicek (1976), Van Es (1992)와 Correa (1995)가 제안한 세가지 추정량들에 대한 소표본 특성을 비교 논의하고자 한다.

• 한국소음진동공학회논문집
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• 제21권5호
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• pp.400-407
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• 2011

The suggested method of previous Son's study dichotomized subjective response data to modeling noise exposure-response. The method used maximum liklihood estimation instead of least square estimation and the noise exposure-response curve of the study was logistic regression analysis result. The method was originated to modeling community response rate such as %HA or %A. It can be useful when the subjective response was investigated based on predicted noise level. It is difficult to measure the single source emitting noise such as railway because various traffic noise sources combined in our life. The suggested method was adopted to model in this study and railway noise-exposure response curves were modeled because the noise level of this area was predicted data. The data of this study was used by previous Ko's paper but he dealt the area as combined noise area and divided the data by dominant noise source. But this study used all data of this area because the annoyance response to railway noise was higher than other noise according to the result of correlation analysis. The trend of the %HA and %A prediction model to train noise of this study is almost same as the model based on measured noise of previous Lim's study although the investigated areas and methods were different.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2009년도 추계학술대회 논문집
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• pp.421-426
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• 2009

This paper presents vibration analysis of maskless exposure module in Printed Circuit Board (PCB) manufacturing system. In order to complete exposure process in PCB, masking type module has been widely used in electronics industries. However, masking process confronts some limitations of application due to higher production cost for masking as well as lower printing resolution. Therefore, maskless exposure module is started to be in the spotlight for flexible production system to meet the needs of fabrication in variable patterns at low cost. Since maskless exposure process adopts direct patterning to PCB, vibration problems become more critical compared to conventional masking type process. Moreover, movements of exposure engine as well as stage generate vibration sources in the system. Thus, it is imperative to analyze the vibration characteristics for the maskless exposure module to improve the quality and accuracy of PCB. In this study, vibration analysis using the Finite Element Analysis is conducted to identify the critical structural parts deteriorating vibration performance. Also, Experimental investigations are conducted by single/dual encoder measurement process under the operating module speed. Measurement points of vibration are selected by three places, which are base of stage, exposure engine and top of stage, to check the effect of vibration from the exposure engine. Comparisons between analysis results and experimental measurement are conducted to confirm the accuracy of analysis results including the developed FE model. Finally, this studies show feasibility of optimal design using the developed FE analysis model.

• 한국소음진동공학회논문집
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• 제19권12호
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• pp.1322-1328
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• 2009

This paper presents vibration analysis of maskless exposure module in printed circuit board(PCB) manufacturing system. In order to complete exposure process in PCB, masking type module has been widely used in electronics industries. However, masking process confronts some limitations of application due to higher production cost for masking as well as lower printing resolution. Therefore, maskless exposure module is started to be in the spotlight for flexible production system to meet the needs of fabrication in variable patterns at low cost. Since maskless exposure process adopts direct patterning to PCB, vibration problems become more critical compared to conventional masking type process. Moreover, movements of exposure engine as well as stage generate vibration sources in the system. Thus, it is imperative to analyze the vibration characteristics for the maskless exposure module to improve the quality and accuracy of PCB. In this study, vibration analysis using the finite element analysis is conducted to identify the critical structural parts deteriorating vibration performance. Also, Experimental investigations are conducted by single/dual encoder measurement process under the operating module speed. Measurement points of vibration are selected by three places, which are base of stage, exposure engine and top of stage, to check the effect of vibration from the exposure engine. Comparisons between analysis results and experimental measurement are conducted to confirm the accuracy of analysis results including the developed FE model. Finally, this studies show feasibility of optimal design using the developed FE analysis model.

• 한국환경보건학회지
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• 제44권4호
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• pp.370-379
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• 2018

Objectives: Bisphenol A (BPA) has been extensively used in a variety of consumer products, resulting in widespread non-occupational human exposure. It is often detected in the human body. Studies have reported many health effects associated with endocrine and metabolic disruptions, including obesity, diabetes, hypertension, and cardiovascular diseases. This study was performed to explain the relationship between BPA exposure and obesity in the Korean adult population. Methods: The second stage of the Korean National Environmental Health Survey (KoNHES) was conducted from 2012 to 2014 with 6,478 persons participating. Using the results of the survey, we analyzed the exposure levels for BPA and the influence on obesity of BPA. Results: In model 1, the volume-based measure concentration of BPA, total, female and the 30s to 60s age group were positively related with BMI. In model 2, creatinine adjusted as a covariate and positive associations for BPA with BMI were observed in the female group and was marginally significantly associated in low body weight group. In model 3, creatinine adjusted (g/g-creatinine), BPA exposure, and BMI were positively related with sex, in females, and there was a marginally significant association with the low body weight group in the BMI categories. BMI was significantly associated with BPA in the female group in all three models. Conclusion: This study added further evidence that exposure to EDCs, include bisphenol A, is related with obesity among the general population. Given the environmental health concerns over BPA, it is necessary to develop comprehensive measures to reduce BPA exposure.

• 대한방사선기술학회지:방사선기술과학
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• 제43권6호
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• pp.503-509
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• 2020

This study aims to develop a patient-specific radiation exposure dose prediction model based on anthropometric data that can be easily measurable during CT examination, and to be used as basic data for DRL setting and radiation dose management system in the future. In addition, among the machine learning algorithms, the most suitable model for predicting exposure doses is presented. The data used in this study were chest CT scan data, and a data set was constructed based on the data including the patient's anthropometric data. In the pre-processing and sample selection of the data, out of the total number of samples of 250 samples, only chest CT scans were performed without using a contrast agent, and 110 samples including height and weight variables were extracted. Of the 110 samples extracted, 66% was used as a training set, and the remaining 44% were used as a test set for verification. The exposure dose was predicted through random forest, linear regression analysis, and SVM algorithm using Orange version 3.26.0, an open software as a machine learning algorithm. Results Algorithm model prediction accuracy was R^2 0.840 for random forest, R^2 0.969 for linear regression analysis, and R^2 0.189 for SVM. As a result of verifying the prediction rate of the algorithm model, the random forest is the highest with R^2 0.986 of the random forest, R^2 0.973 of the linear regression analysis, and R^2 of 0.204 of the SVM, indicating that the model has the best predictive power.

• 대한전자공학회논문지SP
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• 제44권2호
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• pp.93-101
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• 2007

정합과 노출 보정을 동시에 최적화하기 위하여 반복적인 정합 알고리듬인 Lucas-Kanade 알고리듬을 히스토그램 변환에 기초한 노출 보정 알고리듬과 접목하였다. 단순 회귀 모델에 기초하여 비매개변수 추정인 실험적 조건 평균과 그의 다항식 근사를 이용하여 노출 보정을 시도하였다. 제안한 동시 최적화 알고리듬은 각 최적화 과정의 분리화가 가능하므로 기존의 Mann이나 Candocia의 동시 최적화 알고리듬에 비하여 구현의 융통성 측면에서 유리하다. 투사 공간 변환 관계를 가지는 실영상 들을 가지고 모의실험을 수행한 결과에서 보면 노출 보정을 고려하지 않은 경우에 비하여 좋은 성능을 얻음을 확인할 수 있었다.

• 한국산업보건학회지
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• 제26권2호
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• pp.225-236
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• 2016

Objectives: Nanomaterials have been used in various fields. As use of nanoproducts is increasing, workers dealing with nanomaterials are also gradually increasing. Exposure assessments for nanomaterials have been carried out for protection of worker's health in workplace. Exposure studies were mainly focused on manufacturing processes, but these studies on after-treatment processes such as refinement, weighing, and packing were insufficient. So, we investigated exposure characteristics of particles during after-treatment processes of $Al_2O_3$ fibers and Ni powders. Methods: Mass-production of Ni powder process was carried out in enclosed capture-type canopy hood. In a developing stage, $Al_2O_3$ was handled with a local ventilation unit. Exposure characteristics of particles were investigated for $Al_2O_3$ fiber and Ni powder processes during the periods of 10:00 to 16:00, 20 May 2014 and 13:00 to 16:00, 21 May 2014, respectively. Three real-time aerosol instruments were utilized in exposure assessment. A scanning mobility particle sizer(SMPS, nanoscan, model 3910, TSI) and an optical particle counter(OPC, portable aerosol spectrometer, model 1.109, Grimm) were used to determine the particle size distribution in the size range of 10-420 nm and $0.25-32{\mu}m$, respectively. In addition, a nanoparticle aerosol monitor(NAM, model 9000, TSI) was used to measure lung-deposited nanoparticle surface area. Membrane filters(isopore membrane filter, pore size of 100 nm) were also used for air sampling for the FE-SEM(model S-5000H, Hitachi) analysis using a personal sampling pump(model GilAir Plus by 2.5 L/min, Gilian). Conclusions: For Ni powder after-treatment process, only 27% increase in particle concentration was found during the process. However, for $Al_2O_3$ fiber after-treatment process, significant exposure(1.56-3.34 times) was observed during the process.

• 한국안전학회지
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• 제33권4호
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• pp.98-104
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• 2018

Macroscopic accident analyses have been conducted to incorporate transportation safety into long-term transportation planning. In macro-level accident prediction model, exposure variable(e.g. a settled population) have been used as fundamental explanatory variable under the concept that each trip will be subjected to a probable risk of accident. However, a settled population may be embedded error by exclusion of active population concept. The objective of this research study is to develop macro-level accident prediction model using floating population variable(concept of including a settled population and active population) collected from mobile phone data. The concept of accident prediction models is introduced utilizing exposure variable as explanatory variable in a generalized linear regression with assumption of a negative binomial error structure. The goodness of fit of model using floating population variable is compared with that of the each models using population and the number of household variables. Also, log transformation models are additionally developed to improve the goodness of fit. The results show that the log transformation model using floating population variable is useful for capturing the relationships between accident and exposure variable and generally perform better than the models using other existing exposure variables. The developed model using floating population variable can be used to guide transportation safety policy decision makers to allocate resources more efficiently for the regions(or zones) with higher risk and improve urban transportation safety in transportation planning step.