• English Language & Literature Teaching
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• v.18 no.2
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• pp.171-187
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• 2012

This study aims to investigate the effects of learning strategy-based early reading instruction for English underachieving students. For this purpose of the study, sixteen learning strategies were driven from the review of previous related literature and the result of the survey conducted to the students and the teachers. Strategy integrated early reading instruction was implemented to nine students for thirteen weeks. The word recognition test was conducted before and after the instruction to examine whether the instruction had effects on the increase of their early reading skill. In addition, in order to investigate certain change in students' affective aspects after the instruction. The research conducted survey to the students. A teacher's field note and students' class journal were also analyzed to verify the results from the quantitative test. The results indicated that the instruction led to the increase of students' early reading skills. It was also found that the instruction motivated the underachieving students to devise a strategy for their learning process.

• East Asian mathematical journal
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• v.36 no.4
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• pp.437-454
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• 2020

The purpose of this study is to develop a mathematical assessment and correction materials according to the mathematics learning hierarchy. The scope of the study is set to 'function' in 7th grade of middle school. The researchers developed a draft of the mathematical assessment and correction materials based on the mathematics learning hierarchy through the pilot test and the expert review. Using the results of the expert review, the researchers modified and supplemented the math assessment and correction materials to produce the final version. The mathematics assessment and correction material developed in this study is expected to build an effective guidance system for students with mathematics deficits. In addition, by presenting a mathematical assessment and correction materials to the teachers in the field, it is possible to reduce the effort for the management of underachievers and to provide guidance for the education of students with a lack of math learning.

• Speech Sciences
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• v.7 no.3
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• pp.69-85
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• 2000

Recently, Computer Assisted Language Learning (CALL) received widely held attention from diverse audiences. However, to the author's knowledge, relatively little attention was paid to the educational implications of voice recognition (VR) softwares in language teaching in general, and teaching and learning pronunciation in particular. This study explores, and extends the applicability of VR softwares toward second language research areas addressing how VR softwares might facilitate interview data entering processes. To aid the readers' understanding in this field, the background of classroom interaction research, and the rationale of why interview data, therefore the role of VR softwares, becomes critical in this realm of inquiry will be discussed. VR softwares' development and a brief report on the features of up-to-date VR softwares will be sketched. Finally, suggestions for future studies investigating the impact of VR softwares on second language learning, teaching, and research will be offered.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.24.2-24
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• 2001

In this paper, the authors present a row dynamic route planning by Q-learning. The proposed algorithm is executed in a cellular automation based traffic simulator, which is also newly created. In Vehicle Information and Communication System(VICS), which is an active field of Intelligent Transport System(ITS), information of traffic congestion is sent to each vehicle at real time. However, a centralized navigation system is not realistic to guide millions of vehicles in a megalopolis. Autonomous distributed systems should be more flexible and scalable, and also have a chance to focus on each vehicles demand. In such systems, each vehicle can search an own optimal route. We employ Q-learning of the reinforcement learning method to search an optimal or sub-optimal route, in which route drivers can avoid traffic congestions. We find some applications of the reinforcement learning in the "static" environment, but there are ...

• Journal of Fisheries and Marine Sciences Education
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• v.24 no.4
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• pp.528-542
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• 2012

In this study, we designed and applied the learning program of various algorithms about computer science, which were based on computational thinking, to prospective elementary school teachers who were non-majors of this field. While they were learning, they could understand two fundamental functions of computational thinking: abstraction and automation. This learning program made them change their perceptions about computer science positively. They had been interested in learning algorithms and computer science itself, and they felt confident about teaching it.

• Journal of Engineering Education Research
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• v.22 no.1
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• pp.22-28
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• 2019

The purpose of this study is to develop instructional design model of flipped learning suitable for engineering education field and to draw out effects and improvements by applying it to actual lessons for engineering college students. Literature review and case studies were conducted to achieve the purpose of the study. For a case study, flipped learning was applied to 'creative problem solving methodology' which is a liberal arts course of engineering college at D university in Gyeonggi-do. As a result of the literature review, the PARTNER model was applied and weekly instructional guide was presented by each stage. In addition, the results of analysis on the reflection journal showed that the students were more able to achieve the deepening learning stage through active participation in class than the existing class, and found that they had a more challenging plan after the class.

• Journal of the Semiconductor & Display Technology
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• v.17 no.1
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• pp.88-92
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• 2018

Currently drone industry has become one of the fast growing markets and the technology for unmanned aerial vehicles are expected to continue to develop at a rapid rate. Especially small unmanned aerial vehicle systems have been designed and utilized for the various field with their own specific purposes. In these fields the path planning problem to find the shortest path between two oriented points is important. In this paper we introduce a path planning strategy for an autonomous flight of unmanned aerial vehicles through reinforcement learning with self-positioning technique. We perform Q-learning algorithm, a kind of reinforcement learning algorithm. At the same time, multi sensors of acceleraion sensor, gyro sensor, and magnetic are used to estimate the position. For the functional evaluation, the proposed method was simulated with virtual UAV environment and visualized the results. The flight history was based on a PX4 based drones system equipped with a smartphone.

• Journal of the Institute of Convergence Signal Processing
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• v.22 no.1
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• pp.30-37
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• 2021

Accurate 3D object tracking with camera images is a key enabling technology for augmented reality applications. Motivated by the impressive success of convolutional neural networks (CNNs) in computer vision tasks such as image classification, object detection, image segmentation, recent studies for 3D object tracking have focused on leveraging deep learning. In this paper, we review deep learning approaches for 3D object tracking. We describe key methods in this field and discuss potential future research directions.

• Journal of Semiconductor Engineering
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• v.2 no.1
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• pp.125-129
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• 2021

As the interest of deep learning, techniques to control the color of images in image processing field are evolving together. However, there is no clear standard for color, and it is not easy to find a way to represent only the color itself like the color-palette. In this paper, we propose a novel color palette extraction system by chroma fine-tuning with reinforcement learning. It helps to recognize the color combination to represent an input image. First, we use RGBY images to create feature maps by transferring the backbone network with well-trained model-weight which is verified at super resolution convolutional neural networks. Second, feature maps are trained to 3 fully connected layers for the color-palette generation with a generative adversarial network (GAN). Third, we use the reinforcement learning method which only changes chroma information of the GAN-output by slightly moving each Y component of YCbCr color gamut of pixel values up and down. The proposed method outperforms existing color palette extraction methods as given the accuracy of 0.9140.

• International Journal of Advanced Culture Technology
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• v.9 no.1
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• pp.190-195
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• 2021

Currently, one of the fields leading the 4th industrial revolution is the image recognition field of artificial intelligence, which is showing good results in many fields. In this paper, using is a YOLO V2 model, which is one of the image recognition models, we intend to classify and select into three types according to the characteristics of fruits. To this end, it was designed to proceed the number of iterations of learning 9000 counts based on 640 mandarin image data of 3 classes. For model evaluation, normal, rotten, and unripe mandarin oranges were used based on images. We as a result of the experiment, the accuracy of the learning model was different depending on the number of learning. Normal mandarin oranges showed the highest at 60.5% in 9000 repetition learning, and unripe mandarin oranges also showed the highest at 61.8% in 9000 repetition learning. Lastly, rotten tangerines showed the highest accuracy at 86.0% in 7000 iterations. It will be very helpful if the results of this study are used for fruit farms in rural areas where labor is scarce.