• 제목/요약/키워드: 2 phase learning

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딥러닝 기반 레이더 간섭 위상 언래핑 기술 고찰 (A Review on Deep-learning-based Phase Unwrapping Technique for Synthetic Aperture Radar Interferometry)

  • 백원경;정형섭
    • 대한원격탐사학회지
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    • 제38권6_2호
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    • pp.1589-1605
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    • 2022
  • 위상 언래핑은 위성레이더 간섭기법의 필수적인 자료처리 절차다. 이에 따라 비 딥러닝 기반 언래핑 기법이 다수 개발되었으며 최근에는 딥러닝 기반 언래핑 기법이 제안되고 있다. 본 논문에서는 딥러닝 기반 위성레이더 언래핑 기법을 1) 언래핑된 위상의 예측 방법, 2) 위상 언래핑을 위한 딥러닝 모델의 구조 그리고 3) 학습데이터 제작 방법의 측면에서 최근 연구 동향을 소개하였다. 언래핑된 위상을 예측하는 방법은 모호 정수 분류방법, 위상 단절 구간 탐지 방법, 위상 예측 방법, 딥러닝과 전통적인 언래핑 기법의 연계 방법에 따라 다시 세분화하여 연구 동향을 나타냈다. 일반적으로 활용되는 딥러닝 모델 구조의 특징과 전체 위상 정보를 파악하기 위한 모델 최적화 방법에 대한 연구 사례를 소개하였다. 또한 학습데이터 제작 방법은 주로 위상 변이 제작과 노이즈 시뮬레이션 방법으로 구분하여 연구 동향을 정리하였으며 추후 발전 방향을 제시하였다. 본 논문이 추후 국내의 딥러닝 기반 위상 언래핑 연구의 발전 방향을 모색하는 데에 필요한 기반 자료로 활용되기를 기대한다.

학습전략 이러닝 콘텐츠 개발 -스토리텔링을 중심으로- (Development of Learning Strategy e-Learning Contents based on the Storytelling)

  • 박성미
    • 수산해양교육연구
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    • 제24권2호
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    • pp.272-285
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    • 2012
  • The purpose of this study was to develop the Learning Strategy e-Learning Contents based on the storytelling in university students. The objective of the Learning Strategy e-Learning Contents based on the storytelling was to increase in learning skill which university students will use to keep major learning during their courses. The Learning Strategy e-Learning Contents was based on the results of pre-research on storytelling and learning skill. In order to verify the effectiveness of the Learning Strategy e-Learning Contents based on the storytelling, it was analyzed to validity of contents by five professionals. The results of the study were as follows. The Learning Strategy e-Learning Contents based on the storytelling for increasing in learning skill of university students consisted of 15 sessions which proceeding a per semester: the starting phase(1-2), the execution phase(3-13), and the ending phase(14-15). The subjects were 20 university students who had randomly assigned to an experimental group(10) and a control group(10). Subjects completed a learning skill scale. Data analyses were conducted using ANCOVA. The results of the analyses revealed that subjects of experimental group showed significantly higher scores on learning skill than one of control group. Based on the above results, it is concluded that the Learning Strategy e-Learning Contents based on the storytelling was effective in improving learning skill of university students.

퍼지 신경망을 이용한 퍼지 추론 시스템의 학습 및 추론 (Learning and inference of fuzzy inference system with fuzzy neural network)

  • 장대식;최형일
    • 전자공학회논문지B
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    • 제33B권2호
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    • pp.118-130
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    • 1996
  • Fuzzy inference is very useful in expressing ambiguous problems quantitatively and solving them. But like the most of the knowledge based inference systems. It has many difficulties in constructing rules and no learning capability is available. In this paper, we proposed a fuzzy inference system based on fuzy associative memory to solve such problems. The inference system proposed in this paper is mainly composed of learning phase and inference phase. In the learning phase, the system initializes it's basic structure by determining fuzzy membership functions, and constructs fuzzy rules in the form of weights using learning function of fuzzy associative memory. In the inference phase, the system conducts actual inference using the constructed fuzzy rules. We applied the fuzzy inference system proposed in this paper to a pattern classification problem and show the results in the experiment.

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혼종 모형을 이용한 간호 학습전이의 개념 분석 (A Concept Analysis on Learning Transfer in Nursing Using the Hybrid Model)

  • 손해경;김효진;김동희
    • 한국직업건강간호학회지
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    • 제29권4호
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    • pp.354-362
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    • 2020
  • Purpose: This study aimed to define and clarify learning transfer in nursing. Methods: This study used a hybrid model to analyze the concept of learning transfer in nursing through three phases. For the theoretical phase, learning transfer attributes were identified through a scoping literature review. In the fieldwork phase, in-depth focus group interviews were conducted to develop attributes. Purposive sampling was performed with ten participants(five nursing students, two nurses, three nursing faculty members). In the analysis phase, the attributes and final analysis of learning transfer in nursing were extracted and integrated from the previous two phases. Results: According to the analysis, learning transfer was represented in two dimensions with eight attributes. The development of competency dimension had three attributes: 1) theory acquisition, nursing skills, professional attitude, 2) integration, and 3) analysis competency. The competency change dimension had five attributes: 1) appropriateness in patient care, 2) proficiency in patient care, 3) satisfaction, 4) achievement, and 5) confidence. Conclusion: The concept analysis might provide a basic understanding of learning transfer, a development framework toward a measurement of nursing learning transfer and effective educational nursing strategies.

수술실의 간호오류 및 과오 예방을 위한 E-learning 실무교육 프로그램의 개발 및 평가 (Development of an E-learning Education Program for Preventing Nursing Errors and Adverse Events of Operating Room Nurses)

  • 김정순;김명수;황선경
    • 성인간호학회지
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    • 제17권5호
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    • pp.697-708
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    • 2005
  • Purpose: This study was to develop, implement, and evaluate an e-learning education program for improving practical knowledge and preventing nursing errors and adverse events of nurses working in the operating room (OR). Method: The e-learning program was developed and evaluated according to the following processes: 1) preparation phase 2) implementation phase 3) evaluation phase. In evaluation phase, the effectiveness was analyzed based on the Kirkpatrick's model. Results: The e-learning program consisted of OR basic nursing skills and techniques and nursing activities' manual based on the categories of nursing errors: surgical operation preparation, nursing skills and techniques, environment management, patient safety and comfort, and patient monitoring. The program was provided through on-line, http://cafe.daum.net/pnuhorn, for 4 weeks. The mean score(percent) of participants' satisfaction was $21.24{\pm}1.71$(68.2%). Their total knowledge level was significantly improved(Z=-3.00, p=.003) and specifically in the category of environment management(Z=-3.77, p<.001) and patient monitoring(Z=-2.46, p=.014). The occurrence of nursing errors or adverse events was a little decreased, but not statistically significant(Z=-3.10, p=.756). Conclusion: E-learning for nurses is one way of effective and efficient teaching-learning strategies. For better e-learning, it is important to develop the vital content of the education and objective measures for detecting nursing errors and adverse events.

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평지 및 계단 환경에서 보행 속도 변화에 대응 가능한 웨어러블 로봇의 보행 위상 추정 방법 (Gait Phase Estimation Method Adaptable to Changes in Gait Speed on Level Ground and Stairs)

  • 김호빈;이종복;김선우;기인호;김상도;박신석;김강건;이종원
    • 로봇학회논문지
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    • 제18권2호
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    • pp.182-188
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    • 2023
  • Due to the acceleration of an aging society, the need for lower limb exoskeletons to assist gait is increasing. And for use in daily life, it is essential to have technology that can accurately estimate gait phase even in the walking environment and walking speed of the wearer that changes frequently. In this paper, we implement an LSTM-based gait phase estimation learning model by collecting gait data according to changes in gait speed in outdoor level ground and stair environments. In addition, the results of the gait phase estimation error for each walking environment were compared after learning for both max hip extension (MHE) and max hip flexion (MHF), which are ground truth criteria in gait phase divided in previous studies. As a result, the average error rate of all walking environments using MHF reference data and MHE reference data was 2.97% and 4.36%, respectively, and the result of using MHF reference data was 1.39% lower than the result of using MHE reference data.

SOM의 2단계학습을 이용한 항공영상 클러스터링 (Areal Image Clustering using SOM with 2 Phase Learning)

  • 이경희
    • 한국정보통신학회:학술대회논문집
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    • 한국정보통신학회 2013년도 추계학술대회
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    • pp.995-998
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    • 2013
  • 본 논문에서는 자기 조직화 기능을 갖는 Kohonen의 SOM(Self Organization Map) 신경회로망에 2단계의 학습과정을 활용하여 항공영상에서 물체를 인근의 물체와 효과적으로 구별하기 위한 접근방법을 제안하고 실제의 항공영상에 적용하여 기존의 고전적인 K-means 알고리즘 및 원래의 SOM 알고리즘보다 우수함을 보인다.

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Quantification and location damage detection of plane and space truss using residual force method and teaching-learning based optimization algorithm

  • Shallan, Osman;Hamdy, Osman
    • Structural Engineering and Mechanics
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    • 제81권2호
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    • pp.195-203
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    • 2022
  • This paper presents the quantification and location damage detection of plane and space truss structures in a two-phase method to reduce the computations efforts significantly. In the first phase, a proposed damage indicator based on the residual force vector concept is used to get the suspected damaged members. In the second phase, using damage quantification as a variable, a teaching-learning based optimization algorithm (TLBO) is used to obtain the damage quantification value of the suspected members obtained in the first phase. TLBO is a relatively modern algorithm that has proved distinguished in solving optimization problems. For more verification of TLBO effeciency, the classical particle swarm optimization (PSO) is used in the second phase to make a comparison between TLBO and PSO algorithms. As it is clear, the first phase reduces the search space in the second phase, leading to considerable reduction in computations efforts. The method is applied on three examples, including plane and space trusses. Results have proved the capability of the proposed method to precisely detect the quantification and location of damage easily with low computational efforts, and the efficiency of TLBO in comparison to the classical PSO.

공학교육에서의 팀티칭기반 융합프로젝트중심 교수학습모형의 개발 (Teaching-Learning Model of Convergence Project Based on Team Teaching in Engineering Education)

  • 박경선
    • 공학교육연구
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    • 제17권2호
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    • pp.11-24
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    • 2014
  • The purpose of this study is to develop a teaching-learning model of convergence project based on team teaching. Based on development research methodology which explored a university case, the teaching-learning model was developed including three phases such as preparation, planning, and implementation & evaluation. The preparation phase has three steps as follows: to organize team teaching faculty; to develop convergence projects cooperated by industry and university; and to design instructions based on supporting convergence projects. The last step of preparation phase consists of five design activities of: (1) instructions and teaching contents; (2) communication channel among faculty members; (3) feedback system on students' performance; (4) tools to support learners' activity; and (5) evaluation system. The planning phase has two steps to analyze learners and to introduce and modify instruction and themes of convergence projects. The implementation & evaluation phase includes five steps as bellow: (1) to organize project teams and match teams with faculty members; (2) to do team building and assign duties to students of a team; (3) to provide instruction and consulting to teams; (4) to help teams to conduct projects through creative problem solving; and (5) to design mid-term/final presentation and evaluation. Lastly, the research implications and limitations were discussed for future studies.

Predicting compressive strength of bended cement concrete with ANNs

  • Gazder, Uneb;Al-Amoudi, Omar Saeed Baghabara;Khan, Saad Muhammad Saad;Maslehuddin, Mohammad
    • Computers and Concrete
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    • 제20권6호
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    • pp.627-634
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    • 2017
  • Predicting the compressive strength of concrete is important to assess the load-carrying capacity of a structure. However, the use of blended cements to accrue the technical, economic and environmental benefits has increased the complexity of prediction models. Artificial Neural Networks (ANNs) have been used for predicting the compressive strength of ordinary Portland cement concrete, i.e., concrete produced without the addition of supplementary cementing materials. In this study, models to predict the compressive strength of blended cement concrete prepared with a natural pozzolan were developed using regression models and single- and 2-phase learning ANNs. Back-propagation (BP), Levenberg-Marquardt (LM) and Conjugate Gradient Descent (CGD) methods were used for training the ANNs. A 2-phase learning algorithm is proposed for the first time in this study for predictive modeling of the compressive strength of blended cement concrete. The output of these predictive models indicates that the use of a 2-phase learning algorithm will provide better results than the linear regression model or the traditional single-phase ANN models.