• Journal of Engineering Education Research
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• v.18 no.3
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• pp.3-12
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• 2015

The aim of this paper is to provide teacher awareness and the status of robot based instruction(RBI) by focusing on the school curriculum. To gather that information, we conducted a questionnaire survey composed of six items to 116 teachers who have had experiences on RBI. The questions are about the fit school year for RBI, the fit subjects for it, the possibility of applying it to regular subject, the fit students' learning levels for it, the fit learning styles for it and effective methods to apply it to regular subject teachers. The result is as follows: (1) RBI is suitable for fifth and sixth grade in elementary school and all grades in high school. (2) It is suitable for all regular subjects in all schools. (3) It is more effective for the students who have average learning level. (4) It fits into introverted students more than the other style of learners. (5) It is likely to be more effective in supporting of learning and understanding of the contents than merely assisting the teachers' instruction. (6) The teachers showed positive awareness on applying RBI to subject of creative activities. The results are significant in relation to the following two views. First, we can get the positive possibility in applying school curriculum using RBI. Second we can foresee that RBI will provide an innovative paradigm to school curriculum. In addition, the results of this paper can be used as preliminary information for developing models and programs on RBI.

• Journal of the Korean Society of Radiology
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• v.10 no.8
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• pp.637-643
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• 2016

Due to the popularity of artificial intelligent, medical image processing using artificial neural network is increasingly attracting the attention of academic and industry researches. Deep learning with a convolutional neural network has been proved to very effective representation of images. However, the training process requires high performance H/W platform. Thus, the realtime learning of a large number of high dimensional samples within low-power devices is a challenging problem. In this paper, we attempt to establish this possibility by presenting a realtime neural network method on Raspberry pi using online sequential extreme learning machine. Our experiments on high-dimensional dataset show that the proposed method records an almost real-time execution.

• Journal of Fisheries and Marine Sciences Education
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• v.21 no.4
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• pp.523-542
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• 2009

The purpose of this study was to figure out the procedure of learning Korean for international students. A research question was set up as follows: What is the procedure of leaning Korean for international students in Korean universities? To achieve the research purpose, this study implemented a method of semi-constructed interviews. Nineteen international students participated in the interview. The collected data for this study included transcripts from each interview. The transcripts of 60 minutes of interviews with all the participants was audio-taped recorded. This study investigated the research question based on the grounded theory. The analysis of open coding, axial-coding, and selective coding was used in the study. Results indicated that international students learned Korean in a daily basis, and then they adapted to academic Korean in their majored fields. Both personality and mother tongue influenced Korean language learning positively and negatively. International students' improvement of Korean was related in studying with Korean mass media such as TV soap dramas, talk shows, and songs. International students think that TOPIK(Test Of Proficiency In Korean) is not much related with their Korean language fluency. In conclusion, the researchers suggested to give more emphasis on academic training courses for Korean language and to improve the TOPIK in general academic Korean.

• The Journal of Korean Association of Computer Education
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• v.15 no.2
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• pp.19-28
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• 2012

Recent popularity of smart devices and social media seem to increase much interests on social learning, Despite of positive expectation on technology-based social learning, there are not many successful cases and practices of how to apply hands-on technologies and measure educational results. In this study, we tried to promote idea-sharing among learners in a classroom using web-based peer-review of assignments on a specific topic. Then we investigated effects of idea-sharing among learners in terms of individual knowledge construction. Experimental results show that idea-sharing promotes knowledge convergence and divergence, and then knowledge construction at learner's own.

• Journal of Broadcast Engineering
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• v.26 no.5
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• pp.599-607
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• 2021

Inactive region means a region filled with invalid pixel values to represent a specific image. Generally, inactive regions are occurred when the non-rectangular formatted images are converted to the rectangular shaped image, especially when 3D images are represented in 2D format. Because these inactive regions highly degrade the compression efficiency, filtering approaches are often applied to the boundaries between active and inactive regions. However, the image characteristics are not carefully considered during filtering. In the proposed method, inactive regions are padded through reinforcement learning that can consider the compression process and the image characteristics. Experimental results show that the proposed method performs an average of 3.4% better than the conventional padding method.

• Journal of Broadcast Engineering
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• v.26 no.7
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• pp.844-854
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• 2021

Semi-supervised learning (SSL) and few-shot learning (FSL) have shown impressive performance even then the volume of labeled data is very limited. However, SSL and FSL can encounter a significant performance degradation if the diversity gap between the labeled and unlabeled data is high. To reduce this diversity gap, we propose a novel scheme that relies on an autoencoder for generating pseudo examples. Specifically, the autoencoder is trained on a specific class using the available labeled data and the decoder of the trained autoencoder is then used to generate N samples of that specific class based on N random noise, sampled from a standard normal distribution. The above process is repeated for all the classes. Consequently, the generated data reduces the diversity gap and enhances the model performance. Extensive experiments on MNIST and FashionMNIST datasets for SSL and FSL verify the effectiveness of the proposed approach in terms of classification accuracy and robustness against adversarial attacks.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.3
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• pp.215-221
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• 2023

If the C++ programming class, an object-oriented language capable of modeling similar to the real world, is developed as a curriculum that introduces the flipped learning model, students' active problem-solving skills can be cultivated. In this subject development case, it is significant that the flipped learning technique was applied to the programming class and was effective in improving students' active problem-solving skills. First, the lectures in the 4th session were divided into Pre-Class, In-Class, and Post-Class, and the class was conducted in a way that suggested class goals suitable for the subject and formed a team to discuss. At the end of the lecture, a follow-up survey was conducted to check whether the learners learned effectively.

• ETRI Journal
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• v.46 no.2
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• pp.165-174
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• 2024

We propose a method to suppress the speckle noise and blur effects of the light field extracted from a hologram using a deep-learning technique. The light field can be extracted by bandpass filtering in the hologram's frequency domain. The extracted light field has reduced spatial resolution owing to the limited passband size of the bandpass filter and the blurring that occurs when the object is far from the hologram plane and also contains speckle noise caused by the random phase distribution of the three-dimensional object surface. These limitations degrade the reconstruction quality of the hologram resynthesized using the extracted light field. In the proposed method, a deep-learning model based on a generative adversarial network is designed to suppress speckle noise and blurring, resulting in improved quality of the light field extracted from the hologram. The model is trained using pairs of original two-dimensional images and their corresponding light-field data extracted from the complex field generated by the images. Validation of the proposed method is performed using light-field data extracted from holograms of objects with single and multiple depths and mesh-based computer-generated holograms.

• Asia pacific journal of information systems
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• v.32 no.4
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• pp.945-963
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• 2022

With the widespread use of social media, online social platforms like Twitter have become a place of rapid dissemination of information-both accurate and inaccurate. After the COVID-19 outbreak, the overabundance of fake information and rumours on online social platforms about the COVID-19 pandemic has spread over society as quickly as the virus itself. As a result, fake news poses a significant threat to effective virus response by negatively affecting people's willingness to follow the proper public health guidelines and protocols, which makes it important to identify fake information from online platforms for the public interest. In this research, we introduce an approach to detect fake news using deep learning techniques, which outperform traditional machine learning techniques with a 93.1% accuracy. We then investigate the content differences between real and fake news by applying topic modeling and linguistic analysis. Our results show that topics on Politics and Government services are most common in fake news. In addition, we found that fake news has lower analytic and authenticity scores than real news. With the findings, we discuss important academic and practical implications of the study.

• Asia pacific journal of information systems
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• v.34 no.2
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• pp.400-420
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• 2024

Nowadays, social media has evolved into a powerful networked ecosystem in which governments and citizens publicly debate economic and political issues. This holds true for the pros and cons of Indonesia's ore nickel export restriction to Europe, which we aim to investigate further in this paper. Using Twitter as a dependable channel for conducting sentiment analysis, we have gathered 7070 tweets data for further processing using two sentiment analysis approaches, namely Support Vector Machine (SVM) and Long Short Term Memory (LSTM). Model construction stage has shown that Bidirectional LSTM performed better than LSTM and SVM kernels, with accuracy of 91%. The LSTM comes second and The SVM Radial Basis Function comes third in terms of best model, with 88% and 83% accuracies, respectively. In terms of sentiments, most Indonesians believe that the nickel ore provision will have a positive impact on the mining industry in Indonesia. However, a small number of Indonesian citizens contradict this policy due to fears of a trade dispute that could potentially harm Indonesia's bilateral relations with the EU. Hence, this study contributes to the advancement of measuring public opinions through big data tools by identifying Bidirectional LSTM as the optimal model for the dataset.