• Korean journal of communication and information
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• v.60
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• pp.172-192
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• 2012

Over the last ten years, media convergence and multiple platform expansion have affected the ways that people watch conventional television. In the post-TV era, the growing use of the Internet and mobile multi-media devices, such as smart phones, as well as the availability of abundant television content, allows television consumption to be more personalized, diversified, and linked with various media activities, especially social media uses. This study attempts to examine how television viewing experiences have been transformed with the development of the trans-media uses. Based on Walter J. Ong's concept of relation-ism, which posits that new media transform the meanings and relevance of old media rather than making old media obsolete, this study will pay particular attention to how the cultural meanings of television viewing have been redefined in the post-TV era. For the examination, this study has looked at concrete cases of the television viewing experiences of 29 young people in their twenties. Based on in-depth interview data, this study discusses the newly emerging characteristics of television viewing, its temporal and spatial experiences, and the significance of television as a medium and as a social place.

• Journal of Korea Multimedia Society
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• v.9 no.2
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• pp.215-225
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• 2006

Binary images, such as cartoon character images, text images and signature images, which consist of two values with black and white have more difficulties inserting imperceptible secret data than color images. Steganography using binary cover images is not easy to satisfy requirements for both the imperceptibility of stego images and a high embedding rate of secret data at the same time. In this paper, we propose a scheme that can get both the high quality of stego images and a high embedding rate by supplementing the advantages of previous research. In addition, the insertion of the proposed method changes only existing pixels of the imperceptible position and can embed the secret data of [$log_2(mn+1)-2$] bits in a block with size of $m{\times}n$.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.3
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• pp.155-160
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• 2017

Recently, Deep learning has been used successfully to solve many recognition problems. It has many advantages over existing machine learning methods that extract feature points through hand-crafting. Deep neural networks for human activity recognition split video data into frame images, and then classify activities by analysing the connectivity of frame images according to the time. But it is difficult to apply to actual problems which has small-scale activity classes. Because this situations has a problem of overfitting and insufficient training data. In this paper, we defined 5 type of small-scale human activities, and classified them. We construct video database using 700 video clips, and obtained a classifying accuracy of 74.00%.

• The Korean Journal of Applied Statistics
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• v.29 no.5
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• pp.859-872
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• 2016

Land cover classification is an important tool for preventing natural disasters, collecting environmental information, and monitoring natural resources. Hyperspectral imaging is widely used for this task thanks to sufficient spectral information. However, the curse of dimensionality, spatiotemporal variability, and lack of labeled data make it difficult to classify the land cover correctly. We propose a novel classification framework for land cover classification of hyperspectral data based on convolutional neural networks. The proposed framework naturally incorporates full spectral features with the information from neighboring pixels and has advantages over existing methods that require additional feature extraction or pre-processing steps. Empirical evaluation results show that the proposed framework provides good generalization power with classification accuracies better than (or comparable to) the most advanced existing classifiers.

• The Journal of the Acoustical Society of Korea
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• v.39 no.5
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• pp.476-482
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• 2020

Sequence-to-sequence deep neural networks with attention mechanisms have shown superior performance across various domains, where the sizes of the input and the output sequences may differ. However, if the input sequences are much longer than the output sequences, and the characteristic of the input sequence changes within a single output token, the conventional attention mechanisms are inappropriate, because only a single context vector is used for each output token. In this paper, we propose a double-attention mechanism to handle this problem by using two context vectors that cover the left and the right parts of the input focus separately. The effectiveness of the proposed method is evaluated using speech recognition experiments on the TIMIT corpus.

• The Journal of the Acoustical Society of Korea
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• v.36 no.1
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• pp.70-77
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• 2017

Bandwidth extension is a technique to improve speech quality, intelligibility and naturalness, extending from the 300 ~ 3,400 Hz narrowband speech to the 50 ~ 7,000 Hz wideband speech. In this paper, an Artificial Bandwidth Extension (ABE) module embedded in the Opus audio decoder is designed using the information of narrowband speech to reduce the computational complexity of LPC (Linear Prediction Coding) and LSF (Line Spectral Frequencies) analysis and the algorithm delay of the ABE module. We proposed a spectral envelope extension method using DBN (Deep Belief Network), one of deep learning techniques, and the proposed scheme produces better extended spectrum than the traditional codebook mapping method.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.9
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• pp.379-390
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• 2019

In this paper, we propose a novel deep neural network model for Vision-and-Language Navigation (VLN) named LVLN (Landmark-based VLN). In addition to both visual features extracted from input images and linguistic features extracted from the natural language instructions, this model makes use of information about places and landmark objects detected from images. The model also applies a context-based attention mechanism in order to associate each entity mentioned in the instruction, the corresponding region of interest (ROI) in the image, and the corresponding place and landmark object detected from the image with each other. Moreover, in order to improve the success rate of arriving the target goal, the model adopts a progress monitor module for checking substantial approach to the target goal. Conducting experiments with the Matterport3D simulator and the Room-to-Room (R2R) benchmark dataset, we demonstrate high performance of the proposed model.

• The Journal of Engineering Geology
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• v.31 no.1
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• pp.31-42
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• 2021

Database of Cut-slope management system (CSMS) has been constructed based on investigations of all slopes on the roads of the whole country. The investigation data is documented by human, so it is inevitable to avoid human-error such as missing-data and incorrect entering data into computer. The goal of this paper is constructing a prediction model based on several machine-learning algorithms to solve those imperfection problems of the CSMS data. First of all, the character-type data in CSMS data must be transformed to numeric data. After then, two algorithms, i.g., multinomial logistic regression and deep-neural-network (DNN), are performed, and those prediction models from two algorithms are compared. Finally, it is identified that the accuracy of DNN-model is better than logistic model, and the DNN-model will be utilized to improve data-quality.

• Journal of the Korea Society of Computer and Information
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• v.26 no.2
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• pp.177-182
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• 2021

This study was carried out for the purpose of selecting and structuring educational content for personal information protection education in supplementary education for childcare workers. Prior research and literature data were collected and analyzed to select educational content, and a preliminary survey was conducted for 125 applicants for education. Based on the surveyed data, the educational content was structured through focus group interview. In the focus group interview analysis, the person in charge of personal information of the institution and those who have completed education participated. Group interviews and individual interviews through e-mail, etc. were conducted, and the final contents were selected after reviewing the appropriateness of the derived opinions by two educational experts. It was found that the direction of the search for personal information protection education contents should be added to the contents of practical work in each stage of information management and practice such as document writing.

• Tunnel and Underground Space
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• v.32 no.6
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• pp.411-434
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• 2022

In deep geological disposal concept for spent nuclear fuel, it is well-known that rock mass at near-field experiences the thermal-hydraulic-mechanical (THM) coupled behavior. The mechanical properties of rock changes during the coupled process, and it is important to consider the changes into the analysis of numerical simulation and in-situ tests for long-term stability evaluation of nuclear waste disposal repository. This report collected the previous studies on indirect coupled behaviors of rock. The effects of water saturation and temperature on some mechanical properties of rock was considered, while the change in hydraulic conductivity of rock due to stress was included in the indirect coupled behavior.