• 말소리와 음성과학
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• 제14권3호
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• pp.11-25
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• 2022

From simultaneous recordings of the external photoglottography, intra-oral air pressure (P_io), airflow and acoustic data from four native Seoul Korean speakers (2 male and 2 female), we have found that the two fricatives are not significantly different in glottal opening peak and airflow peak height either word-initially or word-medially and that the duration of aspiration is significantly reduced in word-medial /s/, compared to those in word-initial /s/, not in /s'/. We have also found that the duration of a high P_io plateau is significantly longer in /s/ than in /s'/ both word-initially and word-medially and that airflow resistance (R=P_io/U) at the onset and offset of a P_io plateau and at the time of airflow peak height is significantly higher in /s'/ than in /s/ across the contexts. However, the differences in P_io peak and F0 are not significant. In addition, the transition time to reach airflow peak height from the offset of a P_io plateau is found to be significantly longer in /s/ than /s'/ in both word-initial and word-medial positions. No significant differences in glottal opening peak and airflow peak height confirm that /s/ is specified as [-spread glottis] like /s'/. As for the other significant differences, we propose that /s/ is [-tense], and /s'/ [+tense].

• 대한간호학회지
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• 제52권3호
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• pp.261-277
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• 2022

Purpose: This study developed an integrated health management program for metabolic syndrome in psychiatric patients and examined its effects on self-efficacy, healthy lifestyle, physiological indicators, knowledge of metabolic syndrome, attitudes toward healthy behavior, and social support. Methods: A non-equivalent control group pretest posttest design was used. The participants were 65 psychiatric patients with metabolic syndrome in psychiatric rehabilitation centers, with 33 in the experimental group and 32 in the control group. The experimental group participants engaged in daily mobile application and walking exercises three times a week for more than 40 minutes over 8 weeks, while those in the control group were provided education booklets. The outcomes were measured using self-report questionnaires, anthropometrics, and blood analyses. Intervention effects were analyzed using the independent t-test, Mann-Whitney U test, ANCOVA, and Ranked ANCOVA. Results: The experimental group showed a significant increase in self-efficacy (F = 8.85, p = .004, η_p² = .13) and knowledge of metabolic syndrome (t = 2.60, p = .012, d = 0.60) compared to the control group. Additionally, the experimental group demonstrated a significant decrease in waist circumference (Z = - 2.34, p = .009, d = 0.58) and body mass index (Z = - 1.91, p = .028, d = 0.47) compared to the control group. Conclusion: The integrated health management program for psychiatric patients with metabolic syndrome is effective in improving self-efficacy and knowledge of metabolic syndrome and decreasing physiological indicators such as waist circumference and body mass index.

• Smart Structures and Systems
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• 제29권1호
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• pp.237-250
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• 2022

Structural health monitoring (SHM) plays an important role in ensuring the safety and functionality of critical civil infrastructure. In recent years, numerous researchers have conducted studies to develop computer vision and machine learning techniques for SHM purposes, offering the potential to reduce the laborious nature and improve the effectiveness of field inspections. However, high-quality vision data from various types of damaged structures is relatively difficult to obtain, because of the rare occurrence of damaged structures. The lack of data is particularly acute for fatigue crack in steel bridge girder. As a result, the lack of data for training purposes is one of the main issues that hinders wider application of these powerful techniques for SHM. To address this problem, the use of synthetic data is proposed in this article to augment real-world datasets used for training neural networks that can identify fatigue cracks in steel structures. First, random textures representing the surface of steel structures with fatigue cracks are created and mapped onto a 3D graphics model. Subsequently, this model is used to generate synthetic images for various lighting conditions and camera angles. A fully convolutional network is then trained for two cases: (1) using only real-word data, and (2) using both synthetic and real-word data. By employing synthetic data augmentation in the training process, the crack identification performance of the neural network for the test dataset is seen to improve from 35% to 40% and 49% to 62% for intersection over union (IoU) and precision, respectively, demonstrating the efficacy of the proposed approach.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2023년도 학술발표회
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• pp.2-2
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• 2023

기후변화가 가속화로 인해 수재해의 빈도와 강도 예측이 어려워짐에 따라 실시간 홍수 모니터링에 대한 수요가 증가하고 있다. 합성개구레이다는 광원과 날씨에 무관하게 촬영이 가능하여 수재해 발생시에도 영상을 확보할 수 있다. 합성개구레이다를 활용한 수체 탐지 알고리즘 개발이 활발히 연구되어 왔고, 딥러닝의 발달로 CNN을 활용하여 높은 정확도로 수체 탐지가 기능해졌다. 하지만, CNN 기반 수체 탐지 모델은 훈련시 높은 정량적 정확성 지표를 달성하여도 추론 후 정성적 평가시 경계와 소하천에 대한 탐지 정확성이 떨어진다. 홍수 모니터링에서 특히 중요한 정보인 경계와 좁은 하천에 대해서 정확성이 떨어짐에 따라 실생활 적용이 어렵다. 이에 경계를 강화한 적대적 학습 기반의 수체 탐지 모델을 개발하여 더 세밀하고 정확하게 탐지하고자 한다. 적대적 학습은 생성적 적대 신경망(GAN)의 두 개의 모델인 생성자와 판별자가 서로 관여하며 더 높은 정확도를 달성할 수 있도록 학습이다. 이러한 적대적 학습 개념을 수체 탐지 모델에 처음으로 도입하여, 생성자는 실제 라벨 데이터와 유사하게 수체 경계와 소하천까지 탐지하고자 학습한다. 반면 판별자는 경계 거리 변환 맵과 합성개구레이다 영상을 기반으로 라벨데이터와 수체 탐지 결과를 구분한다. 경계가 강화될 수 있도록, 면적과 경계를 모두 고려할 수 있는 손실함수 조합을 구성하였다. 제안 모델이 경계와 소하천을 정확히 탐지하는지 판단하기 위해, 정량적 지표로 F1-score를 사용하였으며, 육안 판독을 통해 정성적 평가도 진행하였다. 기존 U-Net 모델이 탐지하지 못하던 영역에 대해 제안한 경계 강화 적대적 수체 탐지 모델이 수체의 세밀한 부분까지 탐지할 수 있음을 증명하였다.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2023년도 학술발표회
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• pp.137-137
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• 2023

해양쓰레기는 해안의 심미적 가치 저하뿐만 아니라 생태계 파괴, 유령 어업에 따른 수산업 피해 등의 사회적·환경적 문제를 발생시키며, 그중 70% 이상은 육상 기인으로 플라스틱 및 기타 쓰레기가 주를 이루는 해외와 달리 국내의 경우 다량의 초목류를 포함하고 있다. 다양한 부유쓰레기에 대한 기존의 해양쓰레기량 추정의 한계와 하천·하구 쓰레기 수거의 효율화를 위해 해양으로 유입되는 부유쓰레기 방지를 위한 실효성 있는 대책 수립이 필요한 실정이다. 본 연구는 해양 유입 전 하천의 차단시설에 차집된 부유쓰레기의 수거 효율화 및 지속가능한 해양쓰레기 데이터 구축을 위해 AI기반의 기술을 통해 부유쓰레기 성상 분석 기법(Object Detection)과 차집량 분석 기법(Semantic Segmentation)을 활용하였다. 실제와 유사한 데이터 수집을 위해 다양한 하천 환경(정수조, 소하천, 급경사수로)에 대해 탁도(녹조, 유사), 광량, 쓰레기형상, 초목류 함량, 날씨(소하천), 유속(급경사수로) 등의 실험조건에 대하여 해양쓰레기 분류 기준 및 통계를 바탕으로 부유쓰레기 종류 선정하여 학습을 위한 데이터를 수집하였다. 학습 목적에 따라 구분하여 라벨링(Bounding box, Polygon)을 수행하고, 각 분석 기법별 전이학습을 통해 Phase 1(정수조), Phase 2(소하천), Phase 3(급경사수로) 순서로 모델을 고도화하였다. 성상 분석을 위해 YOLO v4를 활용하여 Train, Test DataSet(9:1)을 구성하고 학습 및 평가는 Iteration마다의 mAP, loss 값을 통해 비교하였으며, 학습 Phase에 따라 모델 고도화로 Test Set의 mAP 값이 성상별로 높아짐을 확인하였으며, 차집량 분석을 위해 Unet을 활용하여 Train, Test, Validation DataSet(8.5:1:0.5)을 구성하고 epoch별 IoU(intersection over Union), F1-score, loss 값을 비교하여 정성적, 정량적 평가 모두 Phase 3에서 가장 높은 성능을 확인하였다. 향후 하천 환경에서의 다양한 영양인자별 분석을 통해 주요 영향인자 도출 및 Hyper Parameter 최적화를 통한 모델 고도화로 인해 활용성이 높아질 것으로 판단된다.

• 한국산업융합학회 논문집
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• 제26권3호
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• pp.471-481
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• 2023

Digital twin is considered as a key technology of industry 4.0, thus being essential for the future of industrial production. Despite the significance, a systematic analysis of its technological landscape is lacking. This study aims to investigate the technological development trends and newly emerging technological convergence opportunities in the domain of digital twin by exploiting patent information derived from U SPTO. For this purpose, this study visualized and predicted the convergence dynamics among patent classification codes by adopting patent co-classification analysis and link prediction approach. The findings show that the number of digital twin-related patent applications has increased significantly since 2018. The CPC code G06F showed the highest eigenvector centrality, while G05B was characterized by highest betweenness centrality. According to the predictive model, 41 novel links were revealed, acting as potential technology convergence opportunities. These links were then categorized into 11 different domains. The most dominant category was "digital data processing and artificial intelligence", which could play a foundational role in the diffusion of digital twin technology. The presence of digital twin technology is dominant in manufacturing, but its applications are expected to expand, including "climate change", "healthcare" and "aerospace engineering". The derived insights can support R&D managers and policy makers in formulating R&D strategies and directing future R&D investment decisions.

• Geomechanics and Engineering
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• 제36권3호
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• pp.259-276
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• 2024

The undrained shear strength is widely acknowledged as a fundamental mechanical property of soil and is considered a critical engineering parameter. In recent years, researchers have employed various methodologies to evaluate the shear strength of soil under undrained conditions. These methods encompass both numerical analyses and empirical techniques, such as the cone penetration test (CPT), to gain insights into the properties and behavior of soil. However, several of these methods rely on correlation assumptions, which can lead to inconsistent accuracy and precision. The study involved the development of innovative methods using extreme gradient boosting (XGB) to predict the pile set-up component "A" based on two distinct data sets. The first data set includes average modified cone point bearing capacity (q_t), average wall friction (f_s), and effective vertical stress (σ_vo), while the second data set comprises plasticity index (PI), soil undrained shear cohesion (S_u), and the over consolidation ratio (OCR). These data sets were utilized to develop XGBoost-based methods for predicting the pile set-up component "A". To optimize the internal hyperparameters of the XGBoost model, four optimization algorithms were employed: Particle Swarm Optimization (PSO), Social Spider Optimization (SSO), Arithmetic Optimization Algorithm (AOA), and Sine Cosine Optimization Algorithm (SCOA). The results from the first data set indicate that the XGBoost model optimized using the Arithmetic Optimization Algorithm (XGB - AOA) achieved the highest accuracy, with R2 values of 0.9962 for the training part and 0.9807 for the testing part. The performance of the developed models was further evaluated using the RMSE, MAE, and VAF indices. The results revealed that the XGBoost model optimized using XGBoost - AOA outperformed other models in terms of accuracy, with RMSE, MAE, and VAF values of 0.0078, 0.0015, and 99.6189 for the training part and 0.0141, 0.0112, and 98.0394 for the testing part, respectively. These findings suggest that XGBoost - AOA is the most accurate model for predicting the pile set-up component.

• 한국간호교육학회지
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• 제30권1호
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• pp.5-18
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• 2024

Purpose: This study aimed to identify whether there is a difference between an online-based community psychiatric nursing practice program with the ARCS model and a conventional community psychiatric nursing practice program in promoting nursing students' learning motivation, knowledge of community psychiatric nursing, communication skills, and learning self-efficacy. Methods: This study used a quasi-experimental design with a non-equivalent control group. The participants were 44 nursing students at three nursing colleges in Gyeongsangbuk-do. The experimental group was provided the online-based community psychiatric nursing practice program with ARCS model, while the control group was provided the conventional community psychiatric nursing practice program from July 9, to September 3, 2022. Both groups received practice training 8 hours a day, 5 days two weeks. The collected data were analyzed using the exact χ², Mann-Whitney U-test, and Quade's two-way ANCOVA with the IBM SPSS Statistics 28.0 program. Results: The results of the comparison of an experimental group training with the online-based community psychiatric nursing practice program with ARCS model and a control group training with the conventional community psychiatric nursing practice program showed that, there was no statistically significant difference between the two groups in learning motivation knowledge of community psychiatric nursing, and learning self-efficacy. However, communication skills were statistically significantly higher in the experimental group (F=6.23, p=.017). Conclusion: The online-based community psychiatric nursing practice program with ARCS model can be used as a substitute learning to improve community psychiatric nursing capabilities in situations when clinical practice is suspended due to infectious diseases such as coronavirus disease 2019 or when is a shortage of community psychiatric nursing practice institutions.

• Archives of Plastic Surgery
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• 제51권1호
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• pp.139-145
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• 2024

Background Applying into plastic surgery (PS) is competitive. Lacking a home residency program (HRP) is another barrier. Our goal is to characterize challenges faced by PS applicants without HRPs and identify solutions. Methods Surveys were designed for current integrated PS residents and applicants in the 2022 Match without HRPs. Surveys were distributed electronically. Only U.S. allopathic graduate responses were included. Results Of 182 individuals surveyed, 74 responded (39%, 33 residents, 41 applicants). Sixty-six percent reported feeling disadvantaged due to lack of an HRP. Seventy-six percent of applicants successfully matched. Of these, 48% felt they required academic time off (research year) versus 10% of unmatched applicants. Ninety-seven percent of matched applicants identified a mentor versus 40% of unmatched applicants (p < 0.05). Matched applicants identified mentors through research (29%) and cold calling/emailing (25%). Matched versus unmatched applicants utilized the following resources: senior students (74 vs. 10%, p < 0.05) and social media (52 vs. 10%, p < 0.05). Among residents, 16 had PS divisions (48%). Thirty-six percent with divisions felt they had opportunities to explore PS, compared with 12% without divisions. Residents without divisions felt disadvantaged in finding research (94 vs. 65%, p < 0.05), delayed in deciding on PS (50 vs. 28%), and obtaining mentors (44 vs. 35%) and letters of recommendation (31 vs. 24%). Conclusion PS residents and applicants without HRPs reported feeling disadvantaged when matching. The data suggest that access to departments or divisions assists in matching. We identified that external outreach and research were successful strategies to obtain mentorship. To increase awareness for unaffiliated applicants, we should increase networking opportunities during local, regional, and national meetings.

• 생태와환경
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• 제51권2호
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• pp.149-159
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• 2018

습지 유형에 따른 어류군집 특성을 파악하고 이를 통해 관리 전략을 마련하기 위해 경상남도에 위치한 29개의 습지 (하천형 20개소, 호수형 9개소)를 대상으로 조사를 실시하였다. 조사결과 하천형 습지에서는 평균(${\pm}SD$) $10.3{\pm}4.8$종이, 호수형 습지에서는 평균 $9.1{\pm}4.1$종이 출현하였으며, 출현 종수의 차이는 확인되지 않았다(Mann-Whitney U test, P>0.05). 반면 두 습지 유형의 어류군집을 구성하는 종들은 통계적으로 유의한 수준의 차이를 보였으며(PERMANOVA, Pseudo-F=2.9555, P=0.007), 각 유형의 어류군집에 가장 크게 기여하는 종은 참갈겨니(하천형, 28.51%)와 블루길 (호수형, 23.21%)로 확인되었다 (SIMPER). 지점별 어류군집을 활용한 NMDS 분석결과 총 3개의 그룹(하천형, 호수형, 기타)으로 구분되어 기존의 유형 구분과 차이를 확인할 수 있었다. 현재 습지 관리는 멸종위기종을 중심으로 한 일원화된 방법이 제시되고 있으나, 본 연구에 의하면 습지 유형별 어류군집에 있어서 차이가 있기 때문에 고유종, 외래종, 주요 기여종에 대한 정보를 활용한 관리방법이 마련되어야 한다. 또한 현재 지형을 기반으로 한 습지의 유형 분류가 이루어지고 있으나 일부 습지의 유형에 대한 분류가 모호한 경우가 확인되었으며, 이에 대해 생물상 분석을 통한 보완이 이루어질 필요가 있다. 본 연구는 두 개의 습지 유형에 대한 분석결과로 한계가 있기 때문에 향후 모든 유형의 습지를 대상으로 연구를 실시하여 각 습지의 유형을 대변할 수 있는 세부적인 관리 방법 마련이 이루어져야 할 것이다.