• 융합신호처리학회논문지
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• 제21권4호
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• pp.183-194
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• 2020

최근 영유아 성장기에 발생하는 고관절 이형성증(Developmental Dysplasia of Hip, DDH)의 숫자가 늘어나고 있다. DDH는 영유아 성장을 방해하고 다른 부작용도 많이 발생시키기 때문에 최대한 조기에 발견하여 치료해야 한다. 최근 들어 Convolutional Neural Networks (CNN) 및 개선된 Resnet50을 활용한 머신러닝 기법이 초음파 영상 분석에 많이 활용되고 있다. 연구 결과를 보면 컴퓨터 보조 이미지 분석이 의료현장에서 객관성과 생산성을 크게 향상시키고 있다. 본 연구의 결과는 정형외과에서의 난제인 초음파 영상을 통한 DDH 컴퓨터 보조 진단 알고리즘에도 충분히 활용될 수 있다는 것을 보여주고 있다. 본 논문에서는 CNN을 활용하여 DDH를 자동으로 측정하고 진단할 수 있는 컴퓨터 보조 진단 알고리즘을 제안하였다. DDH 측정을 위해 유아 고관절의 정상/비정상 판독을 위해 Acetabulum-Femoral head의 angle을 자동으로 계산하였으며 기존 영상을 딥 러닝하여 진단을 자동으로 하는 알고리즘을 설계하였다. 실험 결과 의사와 비교하여 진단의 속도와 정확도가 향상된다는 것을 확인하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권5호
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• pp.1610-1629
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• 2021

Failures frequently occurred in manufacturing machines due to complex and changeable manufacturing environments, increasing the downtime and maintenance costs. This manuscript develops a novel deep learning-based method named Multi-Domain Convolutional Neural Network (MDCNN) to deal with this challenging task with vibration signals. The proposed MDCNN consists of time-domain, frequency-domain, and statistical-domain feature channels. The Time-domain channel is to model the hidden patterns of signals in the time domain. The frequency-domain channel uses Discrete Wavelet Transformation (DWT) to obtain the rich feature representations of signals in the frequency domain. The statistic-domain channel contains six statistical variables, which is to reflect the signals' macro statistical-domain features, respectively. Firstly, in the proposed MDCNN, time-domain and frequency-domain channels are processed by CNN individually with various filters. Secondly, the CNN extracted features from time, and frequency domains are merged as time-frequency features. Lastly, time-frequency domain features are fused with six statistical variables as the comprehensive features for identifying the fault. Thereby, the proposed method could make full use of those three domain-features for fault diagnosis while keeping high distinguishability due to CNN's utilization. The authors designed massive experiments with 10-folder cross-validation technology to validate the proposed method's effectiveness on the CWRU bearing data set. The experimental results are calculated by ten-time averaged accuracy. They have confirmed that the proposed MDCNN could intelligently, accurately, and timely detect the fault under the complex manufacturing environments, whose accuracy is nearly 100%.

• 지역사회간호학회지
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• 제31권4호
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• pp.416-426
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• 2020

Purpose: The aim of this study was to evaluate the validity and reliability of the Korean version of Short-form Health Literacy Scale (HLS-SF-K12) for Adults. Methods: The English HLS-SF12 was translated into Korean with forward and backward translation. Survey data were collected from 204 adults who visited two hospitals in Korea. Content validity, construct validity, and known-groups validity were evaluated. Cronbach's α for internal consistency and test-retest were used to assess reliability. SPSS 21.0 and AMOS 21.0 software were used for data analysis. Results: The HLS-SF-K12 was composed of 12 items, and three subscales (health care, disease prevention, and health promotion). The instrument explained reliable internal consistency with Cronbach's α for the total scale of .89, and .74~.81 for subscales. The model of three subscales for the HLS-SF-K12 was validated by confirmatory factor analysis (Normed χ2=2.14 (p<.001), GFI=.92, RMR=.04, RMSEA=.08, CFI=.94, TLI=.92, IFI=.94). The hypothesis testing which analyzed the differences in health literacy by age and education level was satisfied. Conclusion: The HLS-SF-K12 is a valid and reliable instrument for measuring health information comprehension for adults in Korea.

• 여성건강간호학회지
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• 제26권4호
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• pp.326-335
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• 2020

Purpose: This study was conducted to evaluate the construct validity, reliability, measurement invariance, and latent mean differences in the Breastfeeding Adaptation Scale-Short Form (BFAS-SF) for use with mothers at 2 weeks postpartum. Methods: This methodological study was designed to evaluate the validity, reliability, and measurement invariance of the BFAS-SF at 2 weeks postpartum, with data collected from 431 breastfeeding mothers. Confirmatory factor analysis and multi-group confirmatory factor analysis were conducted to assess the factor structure and the measurement invariance across employment status, delivery mode, parity, and previous breastfeeding experience, and the latent mean differences were then examined. Results: The goodness of fit of the six-factor model at 2 weeks postpartum was acceptable. Multi-group confirmatory factor analysis supported strict invariance of the BFAS-SF across employment status and delivery mode. Full configural invariance, full metric invariance, and partial scalar invariance across parity and full configural invariance and full metric invariance across previous breastfeeding experience were supported, respectively. The results for latent mean differences suggested that mothers who were employed showed significantly higher scores for breastfeeding confidence. Mothers who had a vaginal delivery showed significantly higher scores for sufficient breast milk and baby's feeding capability. Multiparous mothers showed significantly higher scores for baby's feeding capability and baby's satisfaction with breastfeeding. Conclusion: The validity and reliability of the BFAS-SF at 2 weeks postpartum are acceptable. It can be used to compare mean scores of breastfeeding adaptation according to employment status, delivery mode, and parity.

• 한국추진공학회지
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• 제24권6호
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• pp.143-154
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• 2020

액체로켓엔진의 추진제 공급시스템의 각 요소와 전체 시스템에 대한 보편적 모델에 실험계수를 적용한 수학적 모델링 기법을 소형 액체로켓엔진을 모사한 수류 시험 장치를 통한 실험 결과로부터 검증하였다. 유체저항요소와 펌프의 압력 변화에 대한 예측을 수행하였으며 예측 정확도 향상을 위해 구성요소 모델링에 대하여 실험계수를 적용하였다. 이를 위해 각 구성요소에 대해 유동의 지배방정식이나 이미 알려진 경험식을 기반으로 실험계수의 도출 방안에 대하여 정리하였으며 사용한 상용품의 실험계수를 제시하였다. 모델링을 통한 예측 결과는 실험 데이터와 비교적 잘 일치하였다. 실험데이터와의 검증을 통해 시뮬레이션의 정확도에 영향을 미치는 인자에 대해 분석하고 시스템 해석의 정확도 향상 방안에 대하여 제안하였다.

• 대한의용생체공학회:의공학회지
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• 제42권4호
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• pp.167-174
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• 2021

The purpose of this study is to classify TIFF images, PNG images, and JPEG images using deep learning, and to compare the accuracy by verifying the classification performance. The TIFF, PNG, and JPEG images converted from chest X-ray DICOM images were applied to five deep neural network models performed in image recognition and classification to compare classification performance. The data consisted of a total of 4,000 X-ray images, which were converted from DICOM images into 16-bit TIFF images and 8-bit PNG and JPEG images. The learning models are CNN models - VGG16, ResNet50, InceptionV3, DenseNet121, and EfficientNetB0. The accuracy of the five convolutional neural network models of TIFF images is 99.86%, 99.86%, 99.99%, 100%, and 99.89%. The accuracy of PNG images is 99.88%, 100%, 99.97%, 99.87%, and 100%. The accuracy of JPEG images is 100%, 100%, 99.96%, 99.89%, and 100%. Validation of classification performance using test data showed 100% in accuracy, precision, recall and F1 score. Our classification results show that when DICOM images are converted to TIFF, PNG, and JPEG images and learned through preprocessing, the learning works well in all formats. In medical imaging research using deep learning, the classification performance is not affected by converting DICOM images into any format.

• Nutrition Research and Practice
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• 제16권2호
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• pp.272-283
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• 2022

BACKGROUND/OBJECTIVES: Most child and adolescent food literacy measurement tools focus on nutrition and food safety. However, the importance of aspects related to the food system such as food distribution and food waste and their effects on environmental sustainability is growing. We therefore developed and validated a two-dimensional tool for children (8-12 years old) and adolescents (13-18 years old) that can comprehensively measure food literacy. The association of food literacy with diet quality and self-reported health was assessed. SUBJECTS/METHODS: First, we developed a food literacy conceptual framework that contains food system and literacy dimensions through a literature review, focus group interviews, and expert review. After a face validity study, we conducted the main survey (n = 200) to validate the questionnaire. Construct validity and reliability were assessed using exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and Cronbach's alpha. RESULTS: As a result of the Delphi study, content validity was confirmed for the remaining 30 items after two items were excluded (content validity ratio = 0.86). Eleven items were excluded from the EFA results, while the CFA results indicated appropriate fit indices for the proposed model (comparative fit index = 0.904, root mean square error of approximation = 0.068). The final food literacy questionnaire consisted of 19 questions and comprised 5 factors: production, distribution, selection, preparation and cooking, and intake. Food literacy was positively associated with diet quality, as assessed by the Nutrition Quotient score, in both children and adolescents and with self-reported health in adolescents.

• 대기
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• 제31권5호
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• pp.489-510
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• 2021

In this paper, we propose an algorithm for detecting convective initiation (CI) using GEO-KOMPSAT-2A/advanced meteorological imager data. The algorithm identifies clouds that are likely to grow into convective clouds with radar reflectivity greater than 35 dBZ within the next two hours. This algorithm is developed using statistical and qualitative analysis of cloud characteristics, such as atmospheric instability, cloud top height, and phase, for convective clouds that occurred on the Korean Peninsula from June to September 2019. The CI algorithm consists of four steps: 1) convective cloud mask, 2) cloud object clustering and tracking, 3) interest field tests, and 4) post-processing tests to remove non-convective objects. Validation, performed using 14 CI events that occurred in the summer of 2020 in Korean Peninsula, shows a total probability of detection of 0.89, false-alarm ratio of 0.46, and mean lead-time of 39 minutes. This algorithm can be useful warnings of rapidly developing convective clouds in future by providing information about CI that is otherwise difficult to predict from radar or a numerical prediction model. This CI information will be provided in short-term forecasts to help predict severe weather events such as localized torrential rainfall and hail.

• 산업융합연구
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• 제20권4호
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• pp.39-46
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• 2022

본 연구는 고등학생이 지각하는 건강관리능력에 미치는 영향요인을 파악하기 위한 서술적 조사연구이다. D시 고등학생 최종 119명을 대상으로 2020년 5월 7일부터 9월 17일까지 진행하였다. SPSS WIN 22.0 프로그램을 이용하여 t-검정, 일원 분산분석, Pearson's correlation coefficients, 위계적 회귀분석을 이용하여 분석하였다. 고등학생이 지각한 건강관리능력은 가족기능의 하위영역인 가족결속력(r=.65, p<.001), 가족적응력(r=.54, p<.001)과 정적인 상관관계가 있었다. 고등학생의 건강관리능력에 유의한 영향요인은 성별(β=.17), 자신의 건강상태 인식(β=.23), 가족결속력(β=.45)으로 확인되었으며, 모형의 설명력은 50%로 나타났다. 추후 고등학생들이 지각하는 가족기능과 고등학생의 건강관리의 실천을 증진시키고, 건강한 성인기를 위한 건강교육 프로그램 중재 및 효과 검증 연구를 제언한다.

• 한국수학교육학회지시리즈E:수학교육논문집
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• 제36권2호
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• pp.201-227
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• 2022

한국 고등학교 학생들에게 적합한 수학불안 요인 측정도구를 개발하기 위하여 요인 탐색, 문항 개발, 타당도 및 신뢰도 검증의 단계로 연구를 실시하였다. 이를 위하여 문헌분석을 통해 기 개발되어 사용되어온 문항을 수정 보완하고, 학생들을 대상으로 한 개방형 조사를 통해 사교육 및 수학 교수 방법에 대한 시대적 배경을 반영한 신규 문항을 추가하였다. 개발된 측정문항에 대한 탐색적 요인분석과 확인적 요인분석을 통해 타당도를 검증하였으며, 연구 결과, 수학교과의 특성에 기인하는 불안 요인이 구체화되었고, 모둠학습이나 발표수업 등과 같은 교수학습 방법 등이 반영되었다.