• Journal of Chest Surgery
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• v.35 no.11
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• pp.785-791
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• 2002

The results of reoperative valve replacement can be improved if appropriate analysis for the risk of reoperation was achieved. The purpose of our study was to analyze the results of reoperations for failure of bioprosthesis, and to define the risk factors in high-risk populations for reoperative procedures. Material and Method The series of 46 consecutive patients who had undergone first reoperative replacement for failed bioprosthesis between 1993 and 2001 were reviewed retrospectively. Mean age was 42 $\pm$ 12 years, mean body surface area was 1.52 $\pm$0.15 $m^2$. The reoperative procedure comprised of 36 MVR, 8 DVR, and 2 AVR. The first operation comprised of 2 DVR, 1 AVR, and 43 MVR. Factors which were choose to assess a predictor of results in reoperative valve replacement were sex, old age(＞60 years), early age at first operation(＜30 years), long interval between first and redo operation(.15years), poor NYHA functional class(＞3), LV dysfunction(LVEF＜45%), long operation time(＞8hours), endocarditis, combined procedures, and renal insufficiency, Result : Overall mortality was 4.3%(2 cases). The risk factors that influenced postoperative complications and unexpected postoperative results were lower ejection fraction(p＝0.012), older age(p＝0.045), endocarditis(p＝0.023), long operation time above 8 hours(p＝0.027). There was no statistically significant factor influencing hospital mortality. Conclusion : No factor influenced the mortality. Better results could be achieved if reoferation was performed carefully in poor left ventricular function, old aged patient, and with endocarditis. Effort to shorten the operation time would be helpful on postoperative results.

• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.1
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• pp.46-56
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• 2018

This paper aimed to identify main factors of community characters, which have an effect on the land surface temperature(LST) change and estimate the impacting coefficient(ratio) of factors in a significant level of statistics. Chungcheongbuk-do province was selected and then partitioned into city and county areas for the sake of convenience of modeling. LST time series data and the community character data were developed based on Terra Satellite MODIS data and collected from the National Statistical Office, respectively. By the cause and effect relationship between community characters and LST, regression coefficients were estimated using a penal model. In a panel modeling, LST and community characters were used as a dependent variable and explanatory variables, respectively. Panel modeling analysis was carried out using statistical package STATA14 and one-way fixed effect model was selected as the most suitable model to evaluate the regression coefficients in the study area. The impacting ratio of LST change by any explanatory variable derived from the regression coefficients of the panel model fixed. Impacting ratios for industrial areas, elevation ${\times}$ building, energy usage, average window speed, non-urban management area, agricultural, nature and environmental conservation, average precipitation were 3.746, 2.856, 2.742, 0.553, 0.102, 0.071 and 0.003, respectively.

• Journal of Korean Society for Geospatial Information Science
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• v.10 no.4 s.22
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• pp.17-30
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• 2002

Since the industrialization spread out, the metropolitan areas of Seoul have been urbanized rapidly in Korea, without concern for the quality of further development and sufficient environmental management. Due to this fact, it has become increasingly apparent that natural hazards, such as floods and landslides, occur frequently after the summer's heavy rains, and because of that, the scale of damage is getting larger. The purpose of this study is first to analyze the relationship between development and floods in the sub-basins of the study area. In addition to this, we'd like to compare the influences of geomorphological characteristics upon the floods occurring in both the whole study area and the developed area in sub-basins.

• Journal of the Korean Society of Hazard Mitigation
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• v.9 no.1
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• pp.135-144
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• 2009

The objective of this study is to examine the effect of hydrological factors on critical durations, and to analyze the relationship between the watershed characteristics and the critical duration of design rainfall in the medium sized catchments. Hydrological factors are used to return period, probable intensity formula, hydrograph method, effective rainfall and temporal pattern of design rainfall. Hydrologic analysis has done over the 44 medium sized catchments with $50{\sim}5,000{\beta}{\yen}$. Watershed characteristics such as catchment area, channel length, channel slope, catchment slope, time to peak, concentration of time and curve number were used to simulate correlation analysis. All of hydrological factors except return period influence to the critical duration of design rainfall. Also, it is revealed that critical duration is influenced by the watershed characteristics such as area, channel length, channel slope and catchment slope. Multiple regression analysis using watershed characteristics is carried out for the estimation of relationship among these. And the 7 type equations are proposed by the multiple regression using watershed characteristics and critical duration of design rainfall. The determination coefficient of multiple regression equations shows $0.96{\sim}0.97$.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.172-172
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• 2022

분산계수는 하천에서 오염물질의 혼합능을 파악할 수 있는 대표적인 인자이다. 특히 하수처리장 방류수 혼합예측과 같이 횡 방향 혼합에 대한 예측이 중요한 경우, 하천의 지형적, 수리학적 특성을 고려한 2차원 횡 분산계수의 결정이 필요하다. 2차원 횡 분산계수의 결정을 위해 기존 연구에서는 추적자실험결과로부터 경험식을 만들어 횡 분산계수 산정에 사용해왔다. 회귀분석을 통한 경험식 산정을 위해서는 충분한 데이터가 필요하지만, 2차원 추적자 실험 건수가 충분치 않아 신뢰성 높은 경험식 산정이 어려운 상황이다. 따라서 본 연구에서는 SMOTE기법을 이용하여 횡분산계수 실험데이터를 증폭시켜 이로부터 횡 분산계수 경험식을 산정하고자 한다. 또한 다중선형회귀분석을 통해 도출된 경험식의 한계를 보완하기 위해 다양한 머신러닝 기법을 적용하고, 횡 분산계수 산정에 적합한 머신러닝 기법을 제안하고자 한다. 기존 추적자실험 데이터로부터 하폭 대 수심비, 유속 대 마찰유속비, 횡 분산계수 데이터 셋을 수집하였으며, SMOTE 알고리즘의 적용을 통해 회귀분석과 머신러닝 기법 적용에 필요한 데이터그룹을 생성했다. 새롭게 생성된 데이터 셋을 포함하여 다중선형회귀분석을 통해 횡 분산계수 경험식을 결정하였으며, 새로 제안한 경험식과 기존 경험식에 대한 정확도를 비교했다. 또한 다중선형회귀분석을 통해 결정된 경험식은 횡 분산계수 예측범위에 한계를 보였기 때문에 머신러닝기법을 적용하여 다중선형회귀분석에 대한 예측성능을 평가했다. 이를 위해 머신러닝 기법으로서 서포트 벡터 머신 회귀(SVR), K근접이웃 회귀(KNN-R), 랜덤 포레스트 회귀(RFR)를 활용했다. 세 가지 머신러닝 기법을 통해 도출된 횡 분산계수와 경험식으로부터 결정된 횡 분산계수를 비교하여 예측 성능을 비교했다. 이를 통해 제한된 실험데이터 셋으로부터 2차원 횡 분산계수 산정을 위한 데이터 전처리 기법 및 횡 분산계수 산정에 적합한 머신러닝 절차와 최적 학습기법을 도출했다.

• Composites Research
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• v.32 no.1
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• pp.78-84
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• 2019

This study focused on the prediction of the long-term performance of carbon fiber/epoxy composites using Dynamic Mechanical Analysis (DMA) and Time-Temperature Superposition (TTS). Single-frequency test, multi-frequency test, and creep TTS test were performed. A sinusoidal load of $20{\mu}m$ amplitude was applied while increasing the temperature from $-30^{\circ}C$ to $240^{\circ}C$ at $2^{\circ}C/min$ for the single-frequency test and the multi-frequency test. The frequencies applied to the multi-frequency test were 0.316, 1, 3.16, 10 and 31.6 Hz. In the creep TTS test, a stress of 15 MPa was applied for 10 minutes at every $10^{\circ}C$ from $-30^{\circ}C$ to $230^{\circ}C$. The glass transition temperature was determined by single-frequency test. The activation energy and the storage modulus curve for each temperature were obtained from glass transition temperature for each frequency by the multi-frequency test. The master curve for the reference temperature was obtained by applying the shift factor using the Arrhenius equation. Also, TTS test was used to obtain the creep compliance curves for each temperature and the master curve for the reference temperature by applying the shift factors using the manual shift technique. The master curve obtained through this process can be applied to predict the long-term performance of carbon fiber/epoxy composites for a given environmental condition.

• Journal of Wetlands Research
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• v.13 no.2
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• pp.171-179
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• 2011

The main objective of this study is to estimate of the vegetation response induced by climate change to soil moisture. We investigated a relationship between vegetation activity and climate variables using Moderate Resolution Imaging Spectroradiometer (MODIS)-retrieved Normalized Difference Vegetation Index (NDVI) and soil moisture. NDVI which extracted from MODIS 13 Vegetation Indices Product was considered as an useful parameter to figure out a relationship with two types of soil moisture, which were observed at Rural Development Administration sites and estimated from Advanced Microwave Scanning Radiometer E (AMSR-E) satellite imagery. The correlation of MODIS-NDVI and ground measured soil moisture were observed, became much stronger when compared to soil moisture values with time lag (5days, 10days, 15days). The correlation patterns between NDVI and soil moisture with different time lag were related to soil texture. The results from this study will be useful to understand the role of vegetation in water balance control in various scales from regional to global climate change.

• Proceedings of the Korean Vacuum Society Conference
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• 2011.02a
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• pp.34-34
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• 2011

본 논문에서는 반도체의 제조공정 중 작동되는 건식 전공펌프에서 측정한 다중변수들의 통계적, 물리적 특성을 소개한다. 흡기부 및 배기부 압력과 부스터/드라이펌프의 소비전류와 같은 상태변수의 변위 분포는 2개 이상의 특징적인 구간으로 나뉘는 특성을 가지고 있다. 특히 흡기부 압력 데이터는 펌프의 성능상태를 직접적으로 나타내는 배기 속도를 유추할 수 있는 특성을 내포하고 있다. 이러한 관측을 통해 발견한 통계학적 특성을 나타내기 위해 적응형 인자모델(APM)을 이용한 진공펌프 시스템의 실시간 진단 기법을 개발하였다. 동시에 공정 중에 배기속도를 유추 할 수 있는 배기속도지표(PSI)를 제안하여 펌프의 성능 상태를 간접적인 방법으로 관찰하는 기법을 개발하여, 두 기법을 통한 진공펌프 시스템의 상태변화 진단 결과의 경향이 동일함을 확인하였다.

• Korean Journal of Clinical Laboratory Science
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• v.50 no.4
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• pp.477-483
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• 2018

Transcranial doppler is a non-invasive method that measures the blood flow velocity and the direction of cerebral blood vessels through the doppler principle. The pulsatility index is an index for measuring the transcranial doppler that reflects the distal vascular resistance and is used as an index for the presence and diffusion of cerebral small vessel diseases. The purpose of this study was to evaluate the risk factors affecting the basilar artery pulsatility index in ischemic stroke patients. From January 2014 to May 2015, 422 patients were selected by measuring the transcranial doppler pulsatility index, considering their basilar artery pulsatility index. Univariate analysis was performed using the basilar artery pulsatility index as a dependent variable. Multiple regression analysis was performed considering the factors affecting the pulsatility index as variables. Univariate analysis revealed age, presence of hypertension, presence of diabetes mellitus, presence of hyperlipidemia, and hematocrit (P<0.1) as factors. Multiple regression analysis showed statistically significant results with age (P<0.001), presence of diabetes (P=0.004), and presence of hyperlipidemia (P=0.041). The risk factors affecting the basilar artery pulsatility index of transcranial doppler were age, diabetes, and hyperlipidemia. Further research will be needed to increase the cerebral pulsatility index as a surrogate marker of the elderly, diabetes, and hyperlipidemia.

• The Korean Journal of Applied Statistics
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• v.33 no.4
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• pp.365-377
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• 2020

We discuss latent factor regression when constructing a common structure inherent among explanatory variables to solve multicollinearity and use them as regressors to construct a linear model of a response variable. Bayesian estimation with LASSO prior of a large penalty parameter to construct a significant factor loading matrix of intrinsic interests among infinite latent structures. The estimated factor loading matrix with estimated other parameters can be inversely transformed into linear parameters of each explanatory variable and used as prediction models for new observations. We apply the proposed method to Product Service Management data of HBAT and observe that the proposed method constructs the same factors of general common factor analysis for the fixed number of factors. The calculated MSE of predicted values of Bayesian latent factor regression model is also smaller than the common factor regression model.