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이현진 ( Lee¸ Hyunjin ),박원배 ( Park¸ Wonbae ) 한국의료복지건축학회 2021 의료·복지 건축 Vol.27 No.3
Purpose: It is important to plan the ward module at a time when the size of beds, the floor area, and the construction budget are all set prior to the hospital design. In this context this study aims (1) to derive various factors affecting the ward module, and (2) to analyze the appropriate room module according to the type. Methods: Design factors related to hospital modules are derived through precedential studies, and the types of ward elevation are classified by reviewing the drawings of 18 case hospitals. And the detailed dimensions and area of the derived elements are analyzed. Results: The X-axis modules of the ward are switched to long span structural columns of 9.9 m, 12.6 m and 13.2 m, but the ward modules still represent 6.6 m. The Y-axis module of the ward shows a dimension of 9 to 9.9m in the process of changing a multi-person room into a four-person room. Type A of curtain wall with columns located on the wall of the room and type B of curtain wall located in the center of the room are analyzed due to their variations. The square window type, which forms the elevation of the square window by exposing the columns to the elevation, and the outframe type, which protrudes from the structural columns and beams, have elevation designs limited. There are, however, no obstacles to the interior space of the hospital room, so the wall composition and furniture arrangement are expected to be free. The ward area of Curtain Wall Type A, which can secure an effective area of 5.9m*5.0m, are 52.1㎡. The Curtain Wall Type A, Square window type, and the outframe type are 49.8㎡. Implications: As part of the hospital standard module plan for economical and reasonable hospital building planning, a type was proposed in this study in conjunction with the external design. It is hoped that it be a base for standard module research linked together to the Central Treatment department, Outpatient department and underground parking lot.
Validation of the oxford classification of iga nephropathy: A Single-Center Study in Korean Adults
( Ho Young Lee ),( Sul Hee Yi ),( Mi Seon Seo ),( Jin Nam Hyun ),( Jin Seok Jeon ),( Hyunjin Noh ),( Dong Cheol Han ),( Seung Duk Hwang ),( So Young Jin ),( Soon Hyo Kwon ) 대한내과학회 2012 The Korean Journal of Internal Medicine Vol.27 No.3
Background/Aims: The recently published Oxford classification of IgA nephropathy (IgAN) proposed a split system for histological grading, based on prognostic pathological features. This new classification system must be validated in a variety of cohorts. We investigated whether these pathological features were applicable to an adult Korean population. Methods: In total, 69 adult Korean patients with IgAN were analyzed using the Oxford classification system at Soonchunhyang University Hospital, Seoul, Korea. All cases were categorized according to Lee`s classification. Renal biopsies from all patients were scored by a pathologist who was blinded to the clinical data for pathological variables. Inclusion criteria were age greater than 18 years and at least 36 months of follow-up. We excluded cases with secondary IgAN, diabetic nephropathy combined other glomerulopathies, less than 36 months of follow-up, and those that progressed rapidly. Results: The median age of the patients was 34 years (range, 27 to 45). Mean arterial blood pressure was 97 ± 10 mmHg at the time of biopsy. The median follow-up period was 85 months (range, 60 to 114). Kaplan-Meier analysis showed significant prognostic predictions for M, E, and T lesions. A Cox proportional hazard regression analysis also revealed prognostic predictions for E and T lesions. Conclusions: Using the Oxford classification in IgAN, E, and T lesions predicted renal outcome in Korean adults after taking clinical variables into account.
XGBoost 기반의 2단계 확률적 일사량 예측과 태양광 예측 알고리즘의 성능 검증
이유림(Yurim Lee),김현진(Hyunjin Kim),이다한(Dahan Lee),이채정(Chaijung Lee),이두희(Duehee Lee) 대한전기학회 2019 전기학회논문지 Vol.68 No.12
We propose the novel solar power forecasting algorithm by using the Extreme Gradient Boosting (XGBoost) machine based on the 2-stage forecasting structure. Our algorithm is implemented to solve three problems. First, the solar power is linearly proportional to the solar irradiation on a target solar panel, but it is hard to obtain the target solar irradiation. Therefore, in the first stage, we predict the target solar irradiation by using the XGBoost based on numerical weather prediction, which is measured on a different location but modified for the target location. Second, the forecasting errors on the predicted solar irradiation can be transferred to the second stage when the predicted solar irradiation is used to predict the solar power. We forecast the conditional error distribution of predicted irradiation by collecting forecasting errors, and we sample solar irradiation scenarios, which are converted to the solar power scenarios. Then, the final point forecast of solar power is estimated by calculating the median of scenarios so that we can improve the forecasting accuracy. Third, in this process, the quality of numerical weather prediction deteriorates as the target hour is farther. Therefore, we build forecasting models for each target hour in parallel to minimize the forecasting accuracy deterioration from the quality deterioration. Finally, we verify our proposed algorithm by participating in the solar power forecasting competition hosted by KPX.
Cancer risk based on alcohol consumption levels: a comprehensive systematic review and meta-analysis
Seunghee Jun(Seunghee Jun),Hyunjin Park(Hyunjin Park),Ui-Jeong Kim(Ui-Jeong Kim),Eun Jeong Choi(Eun Jeong Choi),Hye Ah Lee(Hye Ah Lee),Bomi Park(Bomi Park),Soon Young Lee(Soon Young Lee),Sun Ha Jee(Su 한국역학회 2023 Epidemiology and Health Vol.45 No.-
OBJECTIVES: Alcohol consumption is a well-established risk factor for cancer. Despite extensive research into the relationship between alcohol consumption and cancer risk, the effect of light alcohol consumption on cancer risk remains a topic of debate. To contribute to this discourse, we conducted a comprehensive systematic review and meta-analysis. METHODS: Our systematic review aimed to investigate the associations between different levels of alcohol consumption and the risk of several cancer types. We focused on analyzing prospective associations using data from 139 cohort studies. Among them, 106 studies were included in the meta-analysis after a quantitative synthesis. RESULTS: Our analysis did not find a significant association between light alcohol consumption and all-cause cancer risk (relative risk, 1.02; 95% confidence interval, 0.99 to 1.04), but we observed a dose-response relationship. Light alcohol consumption was significantly associated with higher risks of esophageal, colorectal, and breast cancers. Light to moderate drinking was associated with elevated risks of esophageal, colorectal, laryngeal, and breast cancers. Heavy drinking was also found to contribute to the risk of stomach, liver, pancreas, and prostate cancers, thereby increasing the risk of almost all types of cancer. Additionally, females generally had lower cancer risks compared to males. CONCLUSIONS: Our findings highlight that cancer risks extend beyond heavy alcohol consumption to include light alcohol consumption as well. These findings suggest that there is no safe level of alcohol consumption associated with cancer risk. Our results underscore the importance of public health interventions addressing alcohol consumption to mitigate cancer risks.
Supervised learning에 기반한 돼지 발성음 분류에 관한 연구
민경진(KyoungJin Min),이혁재(HyeokJae Lee),황현진(HyunJin Hwang),이상엽(SangYeob Lee),이강휘(KangHwi Lee),문상호(SangHo Moon),이정은(JungEun Lee),이정환(Jeong Whan Lee) 대한전기학회 2021 전기학회논문지 Vol.70 No.5
This study categorizes the current situation of the pig as supervised learning through the analysis of the pig’s sound. Audio data were obtained from video data obtained by recording at a barn. Speech data was preprocessed to extract features in the time domain and frequency domain, and formants and MFCC were extracted in the frequency domain. Decision Tree, K-Nearest Neighbors, and Support Vector Machine were used for classification, and linear and RBF kernels were used for SVM. The experiment was conducted two times: classifying using features used in Praat and MDVP, which are speech analysis programs, and classifying using MFCC used in speech recognition. After classification, k-fold verification was performed. As a result of the experiment, it can be seen that there is a difference in classification according to the characteristics of using the same voice, and in the case of a situation in which the sound is unified, such as ‘cough,’ the judgment of any classifier is clear. However, in other situations, it is considered necessary to consider the characteristics of pigs by further observing their socialization behavior.
Lee, Geewon,Lee, Ho Yun,Park, Hyunjin,Schiebler, Mark L.,van Beek, Edwin J.R.,Ohno, Yoshiharu,Seo, Joon Beom,Leung, Ann Elsevier 2017 European journal of radiology Vol.86 No.-
<P><B>Abstract</B></P> <P>With the development of functional imaging modalities we now have the ability to study the microenvironment of lung cancer and its genomic instability. <I>Radiomics</I> is defined as the use of automated or semi-automated post-processing and analysis of large amounts of quantitative imaging features that can be derived from medical images. The automated generation of these analytical features helps to quantify a number of variables in the imaging assessment of lung malignancy. These imaging features include: tumor spatial complexity, elucidation of the tumor genomic heterogeneity and composition, subregional identification in terms of tumor viability or aggressiveness, and response to chemotherapy and/or radiation. Therefore, a radiomic approach can help to reveal unique information about tumor behavior. Currently available radiomic features can be divided into four major classes: (a) morphological, (b) statistical, (c) regional, and (d) model-based. Each category yields quantitative parameters that reflect specific aspects of a tumor. The major challenge is to integrate radiomic data with clinical, pathological, and genomic information to decode the different types of tissue biology. There are many currently available radiomic studies on lung cancer for which there is a need to summarize the current state of the art.</P> <P><B>Highlights</B></P> <P> <UL> <LI> Radiomics is the post-processing and analysis of large amounts of quantitative imaging features that can be derived from medical images. </LI> <LI> Radiomics features can reflect the spatial complexity, genomic heterogeneity, and subregional identification of lung cancer. </LI> <LI> Currently available radiomic features can be divided into four major categories. </LI> <LI> The major challenge is to integrate radiomic data with clinical, pathological, and genomic information. </LI> </UL> </P>