• Research in Mathematical Education
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• v.19 no.4
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• pp.211-227
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• 2015

Information Technology influenced on classroom to change the teaching and learning method. Recently, flipped learning method became a hot issue in education by using Information Technology. Learning management system that is introduced in our university in the spring semester 2015, made it possible to apply flipped learning method. So, we used the flipped learning method in a calculus course. In this paper, we found that flipped learning in Calculus we was a little bit affirmative in the aspect of motivation and active learning from students' response on flipped learning method. We analyzed the reason that students were not so positive in continuing flipped learning even though they liked flipped learning a little bit better than traditional learning. We suggest what we pay attention to for applying the flipped learning method effectively.

• Proceedings of the Korea Contents Association Conference
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• 2018.05a
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• pp.423-424
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• 2018

본 연구에서는 학생들의 능동적 학습(Active Learning)의 향상 방안을 모색하고자 하였으며, 이를 위해 학습자 중심의 참여식 교수학습방법을 바탕으로 교수학습모형을 제안하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.12
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• pp.4776-4794
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• 2020

We propose a rapid adaptive learning framework for streaming object detection, called EER-ASSL. The method combines the expected error reduction (EER) dependent rollback learning and the active semi-supervised learning (ASSL) for a rapid adaptive CNN detector. Most CNN object detectors are built on the assumption of static data distribution. However, images are often noisy and biased, and the data distribution is imbalanced in a real world environment. The proposed method consists of collaborative sampling and EER-ASSL. The EER-ASSL utilizes the active learning (AL) and rollback based semi-supervised learning (SSL). The AL allows us to select more informative and representative samples measuring uncertainty and diversity. The SSL divides the selected streaming image samples into the bins and each bin repeatedly transfers the discriminative knowledge of the EER and CNN models to the next bin until convergence and incorporation with the EER rollback learning algorithm is achieved. The EER models provide a rapid short-term myopic adaptation and the CNN models an incremental long-term performance improvement. EER-ASSL can overcome noisy and biased labels in varying data distribution. Extensive experiments shows that EER-ASSL obtained 70.9 mAP compared to state-of-the-art technology such as Faster RCNN, SSD300, and YOLOv2.

• Proceedings of the Korean Information Science Society Conference
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• 2008.06c
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• pp.252-256
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• 2008

본 연구에서는 Active Learning의 학습과정을 변형하여 학습노력을 줄이고 성능향상을 이루는 방법에 대해서 기술한다. Active Learning을 사용하는 이유는 학습 코퍼스의 량을 줄이면서도 우수한 성능을 얻기 위해서이다. 우리는 학습량을 줄이기 위해서 다양성과 대표성이 높은 학습 데이터를 추가한다. 높은 다양성을 얻기 위해서 기 학습된 코퍼스와 가장 관련이 없는 데이터를 추가하고 높은 대표성을 얻기 위해 예제 군집화를 통해 대표적인 예제를 추가할 수 있도록 하였다. 제안된 방법의 효용성을 검사하기 위해서 고정키어구 추출 문제에 적용하였다. 실험결과를 보면 지도학습을 이용한 실험결과보다 우수하였으며, 학습량을 83%정도 줄일 수 있었다.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.429-431
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• 2003

The induced learning retardation method involves the temporary inhibition of the artificial neural network’s active units from participating in the error reduction process during training. This stimulates the less active units to contribute significantly to reduce the network error. However, some less active units are not sensitive to stimulation making them almost useless. The network can then be pruned by removing the less active units to make it smaller and more efficient. This study focuses on making the network more efficient and accurate by developing the induced learning retardation and pruning sequence training method. The developed procedure results to faster learning and more accurate artificial neural network for satellite image classification.

• Journal of Ocean Engineering and Technology
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• v.34 no.4
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• pp.277-284
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• 2020

Underwater acoustics, which is the study of phenomena related to sound waves in water, has been applied mainly in research on the use of sound navigation and range (SONAR) systems for communication, target detection, investigation of marine resources and environments, and noise measurement and analysis. The main objective of underwater acoustic remote sensing is to obtain information on a target object indirectly by using acoustic data. Presently, various types of machine learning techniques are being widely used to extract information from acoustic data. The machine learning techniques typically used in underwater acoustics and their applications in passive SONAR systems were reviewed in the first two parts of this work (Yang et al., 2020a; Yang et al., 2020b). As a follow-up, this paper reviews machine learning applications in SONAR signal processing with a focus on active target detection and classification.

• Communications of the Korean Institute of Information Scientists and Engineers
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• v.30 no.5
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• pp.48-55
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• 2012

• KIPS Transactions on Software and Data Engineering
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• v.2 no.10
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• pp.705-712
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• 2013

Active learning aims to improve the performance of a classification model by repeating the process to select the most helpful unlabeled data and include it to the training set through labelling by expert. In this paper, we propose a method for active learning based on hierarchical agglomerative clustering using Ward's linkage. The proposed method is able to construct a training set actively so as to include at least one sample from each cluster and also to reflect the total data distribution by expanding the existing training set. While most of existing active learning methods assume that an initial training set is given, the proposed method is applicable in both cases when an initial training data is given or not given. Experimental results show the superiority of the proposed method.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.05a
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• pp.485-486
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• 2019

Simulated driving behavior is an important aspect of realistic simulation systems. To simulate natural driving behavior, this paper proposes an imitation learning method based on active learning that combines demonstration and experience. Driving demonstrations are collected from human drivers in a driving simulator. A driving behavior policy is learned from these demonstrations. The driving demonstration dataset is augmented with new demonstrations that the original demonstrations did not contain, in the form of behaviors from another driving behavior policy learned from experience. The final driving behavior policy is learned from an augmented demonstration dataset.

• Journal of Fisheries and Marine Sciences Education
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• v.17 no.2
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• pp.252-259
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• 2005

In this research, as an educational tool in the Refrigeration instruction, a project learning method was applied which enables active hand on practice and encourages a more active participation of students with an increased level of interest in learning. The purpose of this study is to present project learning method as an educational tool that can arouse interest and motivation of students by an active hands-on learning process. This will aid in the enhancement of understanding of educators and students in the project learning method, and assist in development of project learning method that can increase intuitiveness and originality of students. For the purpose of the study, the following questions were asked. First, what is the level of existing knowledge pertaining to Refrigeration Training Program of students? Second, what effect does Refrigeration Training Program conducted via the project approach have on the level of motivation and interest of students? Third, what effect does Refrigeration Training Program conducted via the project approach have on the study habits of students?