• 건강소식
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• v.35 no.2
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• pp.26-27
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• 2011

노인성 난청(presbycusis)은 나이가 들어감에 따라 청력이 점진적으로 악화되는 것으로 성인 난청의 가장 흔한 원인중 하나이다. 미국과 마찬가지로 국내의 여러 연구에서 보면, 65세 이상의 노인 중 보청기를 착용해야 할 정도의 난청이 있는 비율이 전체의 30~35% 정도를 차지하고 있다. 이렇기 때문에 노인성 난청은 노인성 만성 질환 중 퇴행성 관절염, 고혈압에 이어 3대 만성 질환으로 분류되고 있다.

• Journal of Digital Contents Society
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• v.19 no.9
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• pp.1819-1824
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• 2018

This paper proposes a control method of ASMR(autonomous sensory meridian response) contents to relieve user's stress and improve his attention. The proposed method measures EEG(electroencephalography), attention, meditation, and eyeblink data from an EEG device and sends them to an oneM2M-compliant IoT(internet of things) server platform through an Android IoT Application. Then a SVM(support vector machine) model is built to classify user's mental health status by using EEG, attention and meditation data collected in the server platform. The ASMR contents are controlled by the mental health status classified by a SVM model and the eyeblink data. When comparing the SVM models according to types of data used, the SVM model with attention and meditation data showed accuracy of 85.7%. It was verified that the proposed control algorithm of ASMR contents properly worked as the mental health status from the SVM model and the eyeblink data changed.

• Journal of Biomedical Engineering Research
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• v.25 no.2
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• pp.129-135
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• 2004

In the home health management system, we often face the situation to handle biological signals that are frequently measured from normal subjects. In such a case, it is necessary to decide whether the signal at a certain moment is normal or abnormal. Since ECC is one of the most frequently measured biological signals, we describe algorithms that detect QRS-complex and decide whether it is normal or abnormal. The developed QRS detection algorithm is a simplified version of the conventional algorithm providing enough performance for the proposed application. The developed classification algorithm that detects abnormal from mostly normal beats is based on QRS width, R-R interval and QRS shape parameter using Karhunen-Loeve transformation. The simplified QRS detector correctly detected about 99% of all beats in the MTT/BIH ECG database. The classification algorithm correctly classified about 96% of beats as normal or abnormal. The QRS detection and classification algorithm described in this paper could be used in home health management system.

• 동물약계
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• no.50
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• pp.6-6
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• 1998

[ $\cdot$ ]국민의 건강이 경제적 논리보다 우선 $\cdot$축산농가의 이익을 위해서도 `사료첨가제`는 동물약품으로 분류

• Proceedings of the Korean Society of Applied Entomolohy Conference
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• 2006.09a
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• pp.17-26
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• 2006

• THE HEALTH and ORGANIC FARMING
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• no.215
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• pp.9.1-9.1
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• 2005

친환경농업교육에 쿠폰방식의 바우처제 도입 - 친환경농산물 분류 3개로 축소 추진

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.152-152
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• 2023

하천의 건강성을 평가하기 위해 일반적으로 수생태계 건강성 지표(TDI, BMI, FAI, HRI, RVI)가 사용되고 있다. 이 지표는 5가지 등급으로 구분하여 매우 좋음(A), 좋음(B), 보통(C), 나쁨(D), 매우나쁨(E)으로 구분된다. 하지만, 하천의 건강성 관점에서 수질, 토지이용, 지리적 특성, 경관지수와의 상관성을 바탕으로 어떤 영향을 미치는지에 대한 연구가 필요하다. 본 연구에서는 하천의 수생태계 건강성에 영향을 미치는 환경적 인자들과의 관계성을 분석하여 수생태계 건강성이 '좋음'에 해당되는 하천으로 분류하고자 한다. 이를 통해 환경적 인자들의 임계값을 산출하여 하천 관리에 대한 구체적인 우선순위 설정 방안을 제안하고자 한다. 낙동강대권역을 대상으로 수질, 토지이용, 지리적 특성, 경관지수의 여러 변수 중 수생태계 건강성과의 관계에서 대표성을 나타낼 수 있는 환경적 인자를 선정하기 위하여 정준상관분석(CCA)을 수행하였다. 또한 모델 기반의 클러스터 분석을 활용하여 소권역별로 수생태계 건강성이 '좋음'에 해당할 확률을 파악하고, 여기에 해당하는 소권역에 대하여 각각의 환경적 인자에 대한 임계값을 정량적으로 평가하였다. 본 연구에서는 하천의 환경 인자들과의 관계를 분석하여 수생태계 건강성을 평가하고 하천 관리에 대한 구체적인 우선순위를 파악하는 방법을 제안한다. 주성분 분석 및 모델 기반 클러스터 분석을 사용하여 각 소권역에 대한 환경 인자의 임계값을 평가하고, 정책 결정자들이 하천의 건강성을 유지하고 개선할 수 있는 정보를 제공할 수 있다.

• The Korean Journal of Applied Statistics
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• v.35 no.1
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• pp.131-146
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• 2022

Logit models are commonly used to predicting and classifying categorical response variables. Most Bayesian approaches to logit models are implemented based on the Metropolis-Hastings algorithm. However, the algorithm has disadvantages of slow convergence and difficulty in ensuring adequacy for the proposal distribution. Therefore, we use auxiliary mixture sampler proposed by Frühwirth-Schnatter and Frühwirth (2007) to estimate logit models. This method introduces two sequences of auxiliary latent variables to make logit models satisfy normality and linearity. As a result, the method leads that logit model can be easily implemented by Gibbs sampling. We applied the proposed method to diabetes data from the Community Health Survey (2020) of the Korea Disease Control and Prevention Agency and compared performance with Metropolis-Hastings algorithm. In addition, we showed that the logit model using auxiliary mixture sampling has a great classification performance comparable to that of the machine learning models.

• Journal of Preventive Medicine and Public Health
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• v.36 no.4
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• pp.303-313
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• 2003

General information is summarized, that is necessary to introduce a scientific assessment of the human health and exposure issue concerning dioxin and dioxin-like compound. Scientific literatures were reviewed to assess the background exposures to the dioxin-like compounds for normal residents. Epidemiologic studies were also reviewed to assess malignant and nonmalignant sweets of dioxins. In 1997, the International Agency for Research on Cancer classified 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) as a human carcinogen, primarily based on occupational cohort studies. The US Environmental Protection Agency made the same decision in it's Draft Dioxin Reassessment. Epidemiologic evidences point to a generalized excess of all cancers, without any pronounced excess at specific sites. Reported non-cancer effects included a range of conditions affecting most systems. Among them, chloracne, elevation in gamma glutamyl transferase(GGT), and alterations in reproductive hormones are related to TCDO, Other adverse outcomes, such as lipid concentrations, diabetes, circulatory and heart diseases, immunologic disorders, neurobehavioral effects, and developmental outcomes require further study before their respective relationships to TCDD can be more definitively assessed.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.1
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• pp.301-311
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• 2013

This study aims to analyze characteristics, health behaviors and health management level related to private health examination recipients in one general hospital. To achieve this, we analyzed 150,501 cases of private health examination data for 11 years from 2001 to 2011 for 20,696 participants in 2011 in a Dae-Jeon general hospital health examination center. The cluster analysis for classify private health examination group is used z-score standardization of K-means clustering method. The logistic regression analysis, decision tree and neural network analysis are used to periodic/non-periodic private health examination classification model. 1,000 people were selected as a customer management business group that has high probability to be non-periodic private health examination patients in new private health examination. According to results of this study, private health examination group was categorized by new, periodic and non-periodic group. New participants in private health examination were more 30~39 years old person than other age groups and more patients suspected of having renal disease. Periodic participants in private health examination were more male participants and more patients suspected of having hyperlipidemia. Non-periodic participants in private health examination were more smoking and sitting person and more patients suspected of having anemia and diabetes mellitus. As a result of decision tree, variables related to non-periodic participants in private health examination were sex, age, residence, exercise, anemia, hyperlipidemia, diabetes mellitus, obesity and liver disease. In particular, 71.4% of non-periodic participants were female, non-anemic, non-exercise, and suspicious obesity person. To operation of customized customer management business for private health examination will contribute to efficiency in health examination center.