• 근관절건강학회지
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• 제24권3호
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• pp.179-186
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• 2017

Purpose: The purpose of this study was conducted to evaluate the effects of an action learning-based nursing ethics education on the self-assertiveness and ethical values in nursing students. Methods: The study was a non-equivalent control group pretest-posttest design. This study was carried out from October 19 to December 11, 2015. Participants were fifty-six undergraduate nursing students who assigned to either an action learning-based nursing ethics education or traditional lecture. Outcomes were measured assessed self-assertiveness and ethical values using questionnaires. Results: There was a significant improvement in the self-assertiveness in the experimental group who received an action learning-based nursing ethics education than the control group who undertook the traditional lecture (p=.017). However, ethical values were not statistically signigicant between two groups (p=.347). Conclusion: This study demonstrated that an action learning-based nursing ethics education for undergraduate students is very effective in promoting self-assertiveness compared to the traditional lecture.

• 국제학술발표논문집
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• The 9th International Conference on Construction Engineering and Project Management
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• pp.728-735
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• 2022

Roofers get exposed to increased risk of knee musculoskeletal disorders (MSDs) at different phases of a sloped shingle installation task. As different phases are associated with different risk levels, this study explored the application of machine learning for automated classification of seven phases in a shingle installation task using knee kinematics and roof slope information. An optical motion capture system was used to collect knee kinematics data from nine subjects who mimicked shingle installation on a slope-adjustable wooden platform. Four features were used in building a phase classification model. They were three knee joint rotation angles (i.e., flexion, abduction-adduction, and internal-external rotation) of the subjects, and the roof slope at which they operated. Three ensemble machine learning algorithms (i.e., random forests, decision trees, and k-nearest neighbors) were used for training and prediction. The simulations indicate that the k-nearest neighbor classifier provided the best performance, with an overall accuracy of 92.62%, demonstrating the considerable potential of machine learning methods in detecting shingle installation phases from workers knee joint rotation and roof slope information. This knowledge, with further investigation, may facilitate knee MSD risk identification among roofers and intervention development.

• 전기학회논문지P
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• 제59권1호
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• pp.29-34
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• 2010

This paper presents an intelligent learning controller for repetitive walking motion of biped walking robot. The proposed learning controller consists of an iterative learning controller and a direct learning controller. In the iterative learning controller, the PID feedback controller takes part in stabilizing the learning control system while the feedforward learning controller plays a role in compensating for the nonlinearity of uncertain biped walking robot. In the direct learning controller, the desired learning input for new joint trajectories with different time scales from the learned ones is generated directly based on the previous learned input profiles obtained from the iterative learning process. The effectiveness and tracking performance of the proposed learning controller to biped robotic motion is shown by mathematical analysis and computer simulation with 12 DOF biped walking robot.

• 인지과학
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• 제30권1호
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• pp.1-30
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• 2019

인지는 더 이상 개인 내적인 과정으로 개념화 할 수 없으며 학습 또한 마찬가지이다. 협동 및 상호작용에 대한 정보처리적 이해는 아직 부족한 실정인데, 본 논문에서는 학습의 개념이 어떻게 변화해 왔는지 그리고 협동의 과정이 어떠한 정보처리 기제에 의해서 매개되는지 살펴보았다. 협동학습의 주요 인지적 기제로 자원 공유, 구성적 학습 활동의 촉진, 지식 공동 구성, 및 모니터링과 조절 지원을 들 수 있는데, 이들 기제는 학습자 집단을 둘러싼 동기적, 환경적 기제와 상호작용하면서 협동학습의 결과물을 만들어 내는데 기여한다. 테크놀로지의 발달은 협동의 기회를 더욱 확장하고 있는데, 테크놀로지가 협동학습에 제공하는 기능을 7개의 어포던스를 중심으로 살펴보았다. 협업에 대한 보다 정교한 이해를 바탕으로 할 때 협업에 따르는 비용을 줄이면서 협동이 제공하는 다양한 학습 효과를 누리는 것은 물론 협업을 지원하는 효과적인 도구를 개발하는 것이 가능해질 것으로 기대된다.

• 한국항해항만학회지
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• 제38권3호
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• pp.193-202
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• 2014

The comprehensive review of the STCW 1978, as amended in 1995 and associated Code was carried out from 2006 to 2010. These amendments will have a certain degree of impact on Maritime Education and Training(MET) institutes in terms of education and training of seafarer worldwide. Particularly, the Far East region countries are effected more than other regions since they covered about 30% of officers and 37% ratings in the world. In view of these facts this dissertation conceived to analyze the problems in the Far East main seafarer supply countries faced the implementation of "STCW Manila Amendments" To analyze these problems, this dissertation carried out questionnaire research to 7 targeted main MET of major Far East seafarer supply countries. After research this dissertation suggests the possible solutions such as, Joint On-Board Training Center; Joint Asia Maritime E-learning Systems; methods to reducing work-load, ship inspection burden and determine mandatory minimum safety manning standards in a safe way; technical cooperation fund to installation of training equipment; and clarify vague terminology of STCW Manila Amendments, to solve problems identified through the questionnaires.

• Smart Structures and Systems
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• 제32권5호
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• pp.309-318
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• 2023

The appropriate maintenance of highway roads is critical for the safe operation of road networks and conserves maintenance costs. Multiple methods have been developed to investigate the surface of roads for various types of cracks and potholes, among other damage. Like road surface damage, the condition of expansion joints in concrete pavement is important to avoid unexpected hazardous situations. Thus, in this study, a new system is proposed for autonomous expansion joint monitoring using a vision-based system. The system consists of the following three key parts: (1) a camera-mounted vehicle, (2) indication marks on the expansion joints, and (3) a deep learning-based automatic evaluation algorithm. With paired marks indicating the expansion joints in a concrete pavement, they can be automatically detected. An inspection vehicle is equipped with an action camera that acquires images of the expansion joints in the road. You Only Look Once (YOLO) automatically detects the expansion joints with indication marks, which has a performance accuracy of 95%. The width of the detected expansion joint is calculated using an image processing algorithm. Based on the calculated width, the expansion joint is classified into the following two types: normal and dangerous. The obtained results demonstrate that the proposed system is very efficient in terms of speed and accuracy.

• 한국유통학회지:유통연구
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• 제17권3호
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• pp.93-113
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• 2012

갈등에 대한 다양한 연구가 이루어졌음에도 불구하고, 갈등해결을 통한 관계학습의 관점에서 조직적(interorganizational)으로 접근한 연구는 매우 부족한 실정이다. 본 연구에서는 갈등해결 매커니즘을 통해, 유통경로 구성원들이 어떻게 관계학습을 구축할 수 있는지, 그리고 이것들이 경로관계의 성과에 어떠한 영향을 미치는지 살펴보고 있다. 이와 같은 목적으로 국내 유통업체의 협력업체 영업담당자 490명을 대상으로 설문조사를 실시한 결과, 갈등해결에 있어 협력행동은 관계학습의 세 가지 과정인 정보공유, 공동이해와 해석, 관계특유기억 모두를 강화한 반면, 회피행동은 정보공유만 약화시키는 것으로 나타났다. 공동이해와 해석, 관계특유기억은 유통경로의 성과인 효과성과 효율성을 강화시킨 반면, 정보공유는 성과에 영향을 미치지 않았다.

• 한국지진공학회논문집
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• 제28권3호
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• pp.129-139
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• 2024

In this study, the SBC system, a new mechanical joint method, was developed to improve the constructability of precast concrete (PC) beam-column connections. The reliability of the finite element analysis model was verified through the comparison of experimental results and FEM analysis results. Recently, the intermediate moment frame, a seismic force resistance system, has served as a ramen structure that resists seismic force through beams and columns and has few load-bearing walls, so it is increasingly being applied to PC warehouses and PC factories with high loads and long spans. However, looking at the existing PC beam-column anchorage details, the wire, strand, and lower main bar are overlapped with the anchorage rebar at the end, so they do not satisfy the joint and anchorage requirements for reinforcing bars (KDS 41 17 00 9.3). Therefore, a mechanical joint method (SBC) was developed to meet the relevant standards and improve constructability. Tensile and bending experiments were conducted to examine structural performance, and a finite element analysis model was created. The load-displacement curve and failure pattern confirmed that both the experimental and analysis results were similar, and it was verified that a reliable finite element analysis model was built. In addition, bending tests showed that the larger the thickness of the bolt joint surface of the SBC, the better its structural performance. It was also determined that the system could improve energy dissipation ability and ductility through buckling and yielding occurring in the SBC.

• 한국정보과학회 언어공학연구회:학술대회논문집(한글 및 한국어 정보처리)
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• 한국정보과학회언어공학연구회 2020년도 제32회 한글 및 한국어 정보처리 학술대회
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• pp.37-41
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• 2020

본 논문에서는 Joint CTC/Attention 모델에 CTC ratio scheduling을 이용한 end-to-end 한국어 음성인식을 연구하였다. Joint CTC/Attention은 CTC와 attention의 장점을 결합한 모델로서 attention, CTC 단일 모델보다 좋은 성능을 보여주지만, 학습이 진행될수록 CTC가 attention의 학습을 저해하는 요인이 된다. 본 논문에서는 이러한 문제를 해결하기 위해, 학습 진행에 따라 CTC의 비율(ratio)를 줄여나가는 CTC ratio scheduling 방법을 제안한다. CTC ratio scheduling를 이용하여 학습한 결과물은 기존 Joint CTC/Attention, 단일 attention 모델 대비 좋은 성능을 보여주는 것을 확인하였다.

• 국제학술발표논문집
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• The 9th International Conference on Construction Engineering and Project Management
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• pp.871-876
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• 2022

In the construction industry, fatal accidents related to scaffolds frequently occur. To prevent such accidents, scaffolds should be carefully monitored for their safety status. However, manual observation of scaffolds is time-consuming and labor-intensive. This paper proposes a method that automatically analyzes the installation status of scaffold joints based on images acquired from a Unmanned Aerial Vehicle (UAV). Using a deep learning-based object detection algorithm (YOLOv5), scaffold joints and joint components are detected. Based on the detection result, a two-stage rule-based classifier is used to analyze the joint installation status. Experimental results show that joints can be classified as safe or unsafe with 98.2 % and 85.7 % F1-scores, respectively. These results indicate that the proposed method can effectively analyze the joint installation status in UAV-acquired scaffold images.