• Korean Journal of Remote Sensing
• /
• v.38 no.6_2
• /
• pp.1589-1605
• /
• 2022

Phase unwrapping is an essential procedure for interferometric synthetic aperture radar techniques. Accordingly, a lot of phase unwrapping methods have been developed. Deep-learning-based unwrapping methods have recently been proposed. In this paper, we reviewed state-of-the-art deep-learning-based unwrapping approaches in terms of 1) the approaches to predicting unwrapped phases, 2) deep learning model structures for phase unwrapping, and 3) training data generation. The research trend of the approaches to predicting unwrapped phases was introduced by categorizing wrap count segmentation, phase jump classification, phase regression, and deep-learning-assisted method. We introduced the case studies of deep learning model structure for phase unwrapping, and model structure optimization to relate the overall phase information. In addition, we summarized the research trend of the training data generation approaches in the views of phase gradient and noise in the main. And the future direction in deep-learning-based phase unwrapping was presented. It is expected that this paper is used as guideline for exploring future direction of deep-learning-based phase unwrapping research in Korea.

• Journal of Fisheries and Marine Sciences Education
• /
• v.24 no.2
• /
• pp.272-285
• /
• 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.

• Journal of the Korean Institute of Telematics and Electronics B
• /
• v.33B no.2
• /
• pp.118-130
• /
• 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.

• Korean Journal of Occupational Health Nursing
• /
• v.29 no.4
• /
• pp.354-362
• /
• 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.

• Korean Journal of Adult Nursing
• /
• v.17 no.5
• /
• pp.697-708
• /
• 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.

• The Journal of Korea Robotics Society
• /
• v.18 no.2
• /
• pp.182-188
• /
• 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2013.10a
• /
• pp.995-998
• /
• 2013

Aerial imaging is one of the most common and versatile ways of obtaining information from the Earth surface. In this paper, we present an approach by SOM(Self Organization Map) algorithm with 2 phase learning to be applied successfully to aerial images clustering due to its signal-to-noise independency. A comparison with other classical method, such as K-means and traditional SOM, of real-world areal image clustering demonstrates the efficacy of our approach.

• Structural Engineering and Mechanics
• /
• v.81 no.2
• /
• pp.195-203
• /
• 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.

• Journal of Engineering Education Research
• /
• v.17 no.2
• /
• pp.11-24
• /
• 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.

• Computers and Concrete
• /
• v.20 no.6
• /
• pp.627-634
• /
• 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.