• 한국수학교육학회지시리즈A:수학교육
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• 제57권2호
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• pp.75-92
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• 2018

Based on Perceptions about Mathematics Teachers (PMT) perceived by high school students, measured by 2189 students from Seoul Educational Longitudinal Study 2014 (SELS 2014), latent profile analysis (LPA) identified five distinct types of student groups (positive, partial positive, middle, negative, extreme negative). These student of positive, middle, and negative groups are positive, moderate and negative perceptions about math teachers. Partial positive group generally had a positive perception about mathematics teachers, extremely negative group was very negative about mathematics teachers. Both of these groups had peculiarly inconsistent trends and several anomalies. The Multinomial logistic regression analyses also indicated that individual factors (gender, major, self-concept, resilience, self-assessment, career maturity), school factors (friendship, relationship with school teachers) and parental factors (academic-relationship, emotional-relationship) were significant predictors of PMT profile groups. The Analysis of variance also indicated that mathematics class (attitude, satisfaction and atmosphere), Mathematics achievement were significant predictors of PMT profile groups. The profiling of perceptions about mathematics teachers resulted in enhanced understanding of the complex range of processes students employed. During mathematics class, implementation of smooth interactions and communications between students and teachers added in the teaching and learning of mathematics.

• 가정∙방문간호학회지
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• 제22권1호
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• pp.59-68
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• 2015

Purpose: The purpose of this study was to analyze nursing research using data envelopment analysis and suggest directions for future research. Methods: We established -criteria literature search. e reviewed 45 from RISS, KISS, National assembly library and NDSL DB. Data were collected on December 17, 2013. developed analytic framework of literature reviews using Yun's study. This framework had 8 items related to approach of data envelopment analysis. Results: literature established -criteria. Average numbers of input and output variables were 2.4 and 4.2, respectively. All selected research conducted efficiency analysis, analysis, and inefficiency analysis. However only 3 research. Conclusion: he results of studysuggest that data envelopment are needed to enhance efficiencies of nursing organization as follows individual nurse's profiling to develop customized performance management plans; patient centered nursing interventions; and financial performance financial reports.

• Genomics & Informatics
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• 제6권3호
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• pp.126-129
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• 2008

The robust identification and comprehensive profiling of copy number alterations (CNAs) is highly challenging. The amount of data obtained from high-throughput technologies such as array-based comparative genomic hybridization is often too large and it is required to develop a comprehensive and versatile tool for the detection and visualization of CNAs in a genome-wide scale. With this respective, we introduce a software framework, CGHscape that was originally developed to explore the CNAs for the study of copy number variation (CNV) or tumor biology. As a standalone program, CGHscape can be easily installed and run in Microsoft Windows platform. With a user-friendly interface, CGHscape provides a method for data smoothing to cope with the intrinsic noise of array data and CNA detection based on SW-ARRAY algorithm. The analysis results can be demonstrated as log2 plots for individual chromosomes or genomic distribution of identified CNAs. With extended applicability, CGHscape can be used for the initial screening and visualization of CNAs facilitating the cataloguing and characterizing chromosomal alterations of a cohort of samples.

• 식품과학과 산업
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• 제54권3호
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• pp.145-159
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• 2021

Conventional nutrition services involve producer-oriented approaches without considering the differences in the characteristics and circumstances of each individual, whereas personalized nutrition services are consumer-oriented concepts that provide products and services for maintaining optimal health conditions based on the genetic, physiological, and metabolic characteristics of individuals, with these products based on balanced nutrition and healthy living. Currently, methods for evaluating dietary habits, monitoring dietary behaviors, deep phenotyping, and metabotyping via microbiota profiling, as well as methods for predicting big data by using machine learning, have been previously studied in Korea and abroad. With the development of medical technology and the improvement of hygiene, the demand for personalized nutrition and health services for healthier, happier, and more satisfying lives is rapidly increasing. Therefore, based on scientific technologies, attempts are needed to advance these services into global personalized markets and to boost the global competitiveness of countries and companies.

• 식품과학과 산업
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• 제50권1호
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• pp.2-10
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• 2017

Human have eaten various traditional fermented foods for a numbers of million years for health benefit as well as survival. The beneficial effects of fermented foods have been resulted from complex microbial communications within the fermented foods. Therefore, the holistic approaches for individual identification and complete microbial profiling involved in their communications have been of interest to food microbiology fields. Microbiome is the ecological community of microorganisms that literally share our environments including foods as well as human body. However, due to the limitation of culture-dependent methods such as simple isolations of just culturable microorganisms, the culture-independent methods have been consistently developed, resulting in new light on the diverse non-culturable and hitherto unknown microorganisms, and even microbial communities in the fermented foods. For the culture-independent approaches, the food microbiome has been deciphered by employing various molecular analysis tools such as fluorescence in situ hybridization, quantitative PCR, and denaturing gradient gel-electrophoresis. More recently, next-generation-sequencing (NGS) platform-based microbiome analysis has been of interest, because NGS is a powerful analytical tool capable of resolving the microbiome in respect to community structures, dynamics, and activities. In this overview, the development status of analysis tools for the fermented food microbiome is covered and research trend for NGS-based food microbiome analysis is also discussed.

• ETRI Journal
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• 제45권2호
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• pp.318-328
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• 2023

Recently, embedded systems, such as mobile platforms, have multiple processing units that can operate in parallel, such as centralized processing units (CPUs) and neural processing units (NPUs). We can use deep-learning compilers to generate machine code optimized for these embedded systems from a deep neural network (DNN). However, the deep-learning compilers proposed so far generate codes that sequentially execute DNN operators on a single processing unit or parallel codes for graphic processing units (GPUs). In this study, we propose PartitionTuner, an operator scheduler for deep-learning compilers that supports multiple heterogeneous PUs including CPUs and NPUs. PartitionTuner can generate an operator-scheduling plan that uses all available PUs simultaneously to minimize overall DNN inference time. Operator scheduling is based on the analysis of DNN architecture and the performance profiles of individual and group operators measured on heterogeneous processing units. By the experiments for seven DNNs, PartitionTuner generates scheduling plans that perform 5.03% better than a static type-based operator-scheduling technique for SqueezeNet. In addition, PartitionTuner outperforms recent profiling-based operator-scheduling techniques for ResNet50, ResNet18, and SqueezeNet by 7.18%, 5.36%, and 2.73%, respectively.

• 대기
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• 제34권3호
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• pp.325-336
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• 2024

Radiosonde is an important in-situ profiling instrument that measures atmospheric temperature, moisture, and wind structure from the surface to the middle stratosphere. The operational radiosonde measurements are carried out more than twice (at 0000 UTC and 1200 UTC) daily at approximately 1,300 World Meteorological Organization (WMO) stations and play a pivotal role in daily weather forecasts. It also contributes to the monitoring of atmospheric structure by providing the key physical information like temperature and pressure, forming the backbone of atmospheric (re)analyses and numerical weather forecasts. Additionally, high-resolution radiosonde profiles are used for calibration and evaluation of satellite products. Despite these advantages, radiosonde measurements are mostly limited to operational uses due to the high initial cost of ground instrument setup required for data transmission and reception. This study outlines a cost-effective (roughly one-tenth of the operational cost) method for establishing the ground station and the necessary radiosonde measurement procedures, offering guidance for individual researchers or university-level instructors.

• 지구물리와물리탐사
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• 제18권4호
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• pp.181-196
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• 2015

3.5 kHz 또는 첩(chirp) 천부해저 지층탐사는 해양지질 조사나 엔지니어링 탐사에 널리 사용되고 있다. 그러나 해상에서의 너울은 탐사자료의 품질을 저하시킨다. 이와 같은 너울의 영향을 보정함으로써 연속성이 향상된 탐사자료를 얻을 수 있다. 정확한 해저면의 위치 선정은 너울영향 보정에 매우 중요하다. 이 연구에서는 원자료와 이를 엔벨로프 또는 에너지비율자료로 변형시킨 자료들에 대해 최대 진폭값의 일정 기준을 초과하는 지점을 선정하는 방법으로 해저면 위치 선정을 시도하였다. 그러나 파도의 잡음으로 인하여 해저면 신호가 분명하지 않은 품질이 낮은 자료에서는 개별 트레이스에서의 자동적인 해저면 위치 선정이 어려웠다. 이 연구에서는 이전 트레이스에서 구한 해저면 평균값을 고려하여 해저면 선정범위 내에서 해저면을 선정하는 방법과, 선정 결과의 신뢰도가 낮은 경우에는 이를 보정에서 제외하는 방법을 사용함으로써 품질이 낮은 자료의 해저면 선정에서도 만족스러운 결과를 얻었다. 개별 트레이스에서 해저면을 선정할 때에는 에너지비율자료를 사용한 경우에 오류가 가장 적었으며, 이전 트레이스 해저면 평균값을 고려하는 방법에서는 원자료를 직접 사용한 경우에 보정결과가 비교적 양호하였다.

• Asian Journal of Atmospheric Environment
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• 제6권2호
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• pp.73-82
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• 2012

This study was aimed to thoroughly estimate the characteristics of indoor particulate matter (PM) collected on subway platforms by the cooperative approach of semi-bulk and single particle analyses. The size-resolved PM and its number concentration were measured on the platform in a heavily traveled subway station in Fukuoka, Japan. Particle Induced X-ray Emission (PIXE) and micro-PIXE techniques were applied to the chemical analyses of semi-bulk and single particle, respectively. There was the close resemblance of timely fluctuation between PM number concentration and train service on the third basement floor (B3F) platform compared to the second basement floor (B2F) and its maximum level was marked in rush hour. Higher number counts in large particles ($>1{\mu}m$) and lower number counts in fine particles ($<1{\mu}m$) were shown on the platform compared to an above ground. PM2.5 accounted for 58.2% and 38.2 % of TSP on B3F and on B2F, respectively. The elements that were ranked at high concentration in size-resolved semi-bulk PM were Fe, Si, Ca, S, and Na. The major elements tending to have more elevated levels on B3F than B2F were Fe (4.4 times), Ca (17.3 times), and Si (46.4 times). Although concentrations were very low, Cr ($11.9ng\;m^{-3}$ on B3F, $2.4ng\;m^{-3}$ on B2F), Mn ($3.4ng\;m^{-3}$ on B3F, $0.9ng\;m^{-3}$ on B2F), and Pb ($0.6ng\;m^{-3}$ on B3F, $1.6ng\;m^{-3}$ on B2F) were detected from PM2.5. Individual PM was nearly all enriched in Fe with Si and Ca. Classifying and source profiling of the individual particles by elemental maps and particle morphology were tried and particles were presumably divided into four groups (i.e., train/rail friction, train-rail sparking, ballast/abrasive, and cement).

• 보건행정학회지
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• 제27권2호
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• pp.149-156
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• 2017

Background: This study was conducted to evaluate the performance of the Hierarchical Condition Category (HCC) model, identify potentially high-cost patients, and examine the effects of adding prior utilization to the risk model using Korean claims data. Methods: We incorporated 2 years of data from the National Health Insurance Services-National Sample Cohort. Five risk models were used to predict health expenditures: model 1 (age/sex groups), model 2 (the Center for Medicare and Medicaid Services-HCC with age/sex groups), model 3 (selected 54 HCCs with age/sex groups), model 4 (bed-days of care plus model 3), and model 5 (medication-days plus model 3). We evaluated model performance using $R^2$ at individual level, predictive positive value (PPV) of the top 5% of high-cost patients, and predictive ratio (PR) within subgroups. Results: The suitability of the model, including prior use, bed-days, and medication-days, was better than other models. $R^2$ values were 8%, 39%, 37%, 43%, and 57% with model 1, 2, 3, 4, and 5, respectively. After being removed the extreme values, the corresponding $R^2$ values were slightly improved in all models. PPVs were 16.4%, 25.2%, 25.1%, 33.8%, and 53.8%. Total expenditure was underpredicted for the highest expenditure group and overpredicted for the four other groups. PR had a tendency to decrease from younger group to older group in both female and male. Conclusion: The risk adjustment models are important in plan payment, reimbursement, profiling, and research. Combined prior use and diagnostic data are more powerful to predict health costs and to identify high-cost patients.