• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.873-875
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• 2013

정보기술 발전에 따라 정보 활용 및 처리 역량이 상승하면서 교육 환경의 지능화, 네트워크화로 기술간, 서비스 간 융 복합을 통한 다양한 학습 내용 및 방법이 출현하였으며, 최근 e-러닝 산업에서 스마트기기 보급 확산과 상황 적응적이고 자기 주도적 학습에 대한 소비자의 니즈가 증가하면서 새로운 형태의 교육시스템인 스마트러닝이 부각되고 있다. 이러한 교육 패러다임의 변화에 따라 기존의 교육 콘텐츠를 스마트기기에 적용하기 위해서는 콘텐츠 및 솔루션 구조의 개선이 요구되며, 또한 서비스 제공의 측면에서 다양한 교육 콘텐츠 연동과 교육 서비스 융합을 위한 표준 플랫폼 적용이 필요하다. 이에 본 논문에서는 JVM 환경의 PC 인터페이스를 통해 ePub 표준의 교육용 멀티미디어 콘텐츠 제작기능과 기존 서책형 파일 포맷의 자료 정보를 응용하기 위한 정보변환 모듈, 스마트 기기용 ePub 전자책 뷰어를 포함하는 통합 솔루션 소프트웨어인 ePub Solution을 설계하였다.

• Journal of Information Processing Systems
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• v.16 no.3
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• pp.718-732
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• 2020

Conducting sentiment analysis and opinion mining are challenging tasks in natural language processing. Many of the sentiment analysis and opinion mining applications focus on product reviews, social media reviews, forums and microblogs whose reviews are topic-similar and opinion-rich. In this paper, we try to analyze the sentiments of sentences from online webcast reviews that scroll across the screen, which we call live barrages. Contrary to social media comments or product reviews, the topics in live barrages are more fragmented, and there are plenty of invalid comments that we must remove in the preprocessing phase. To extract evaluative sentiment sentences, we proposed a novel approach that clusters the barrages from the same commenter to solve the problem of scattering the information for each barrage. The method developed in this paper contains two subtasks: in the data preprocessing phase, we cluster the sentences from the same commenter and remove unavailable sentences; and we use a semi-supervised machine learning approach, the naïve Bayes algorithm, to analyze the sentiment of the barrage. According to our experimental results, this method shows that it performs well in analyzing the sentiment of online webcast barrages.

• Geomechanics and Engineering
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• v.18 no.6
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• pp.595-603
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• 2019

A modified model combining Kriging and Monte Carlo method (MC) is proposed for probabilistic estimation of tunnel face stability in this paper. In the model, a novel uniform design is adopted to train the Kriging, instead of the existing active learning function. It has advantage of avoiding addition of new training points iteratively, and greatly saves the computational time in model training. The kinematic approach of limit analysis is employed to define the deterministic computational model of face failure, in which the Hoek-Brown failure criterion is introduced to account for the nonlinear behaviors of rock mass. The trained Kriging is used as a surrogate model to perform MC with dramatic reduction of calls to actual limit state function. The parameters in Hoek-Brown failure criterion are considered as random variables in the analysis. The failure probability is estimated by direct MC to test the accuracy and efficiency of the proposed probabilistic model. The influences of uncertainty level, correlation relationship and distribution type of random variables are further discussed using the proposed approach. In summary, the probabilistic model is an accurate and economical alternative to perform probabilistic stability analysis of tunnel face excavated in spatially random Hoek- Brown media.

• International Journal of Internet, Broadcasting and Communication
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• v.11 no.3
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• pp.106-110
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• 2019

The interaural level difference (ILD) used for the sound localization using stereo signals is to find the difference in energy that the sound source reaches both ears. The conventional ILD does not consider the time difference of the stereo signals, which is a factor of lowering the accuracy. In this paper, we propose a synchronized ILD that obtains the ILD after synchronizing these time differences. This method uses the cross-correlation function (CCF) to calculate the time difference to reach both ears and use it to obtain synchronized ILD. In order to prove the performance of the proposed method, we conducted two sound localization experiments. In each experiment, the synchronized ILD and CCF or only the synchronized ILD were given as inputs of the deep neural networks (DNN), respectively. In this paper, we evaluate the performance of sound localization with mean error and accuracy of sound localization. Experimental results show that the proposed method has better performance than the conventional methods.

• Cross-Cultural Studies
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• v.25
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• pp.549-596
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• 2011

This paper aims to introduce some useful multimedia freewares, and also support Chinese instructor with discussing how to operate them for instructional design of multimedia language learning class. For this aims, this paper consists of three parts: First, instructional design. This part is focused to what kind of instructional model to be based on, for example, Dick & Carey model, ADDIE model, ASSURE model etc. This part introduces these models, and modifies ADDIE and ASSURE model to D.D.A.I.E.S and S.S.A.U.R.E.S as 'A(nalysis)' in these model may apply to the next 'D(evelopment)' on ADDIE, 'S(elect Methods, Media and Materials)' on ASSURE in the practical Chinese class. Second, Programme: What to use. This part is focused to what kind of free software we can use. In the web site online, there are huge free softwares so we usually hesitate to select and also don't know how to operate even though selected one of them. This part, accordingly, introduces ten of useful freewares and compares each other in terms of usefulness for Chinese instructors. Third, Programme: How to use. It is of no use just to know what to use but not to know how to operate, so this part describes how to use freewares like a kind of manual in detail as far as possible. In conclusion, we hope more Chinese instructors to learn and use more useful freewares for designing the better multimedia Chinese class by this paper.

• Smart Media Journal
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• v.10 no.1
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• pp.79-87
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• 2021

In recent years research on automatic font generation with machine learning mainly focus on using transformation-based methods, in comparison, generative model-based methods of font generation have received less attention. Transformation-based methods learn a mapping of the transformations from an existing input to a target. This makes them ambiguous because in some cases a single input reference may correspond to multiple possible outputs. In this work, we focus on font generation using the generative model-based methods which learn the buildup of the characters from noise-to-image. We propose a novel way to train a conditional generative deep neural model so that we can achieve font style control on the generated font images. Our research demonstrates how to generate new font images conditioned on both character class labels and character style labels when using the generative model-based methods. We achieve this by introducing a modified generator network which is given inputs noise, character class, and style, which help us to calculate losses separately for the character class labels and character style labels. We show that adding the character style vector on top of the character class vector separately gives the model rich information about the font and enables us to explicitly specify not only the character class but also the character style that we want the model to generate.

• Smart Media Journal
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• v.9 no.4
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• pp.17-25
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• 2020

In the image object classification problem, low-resolution images may have a negative impact on the classification result, especially when the classification method, such as a convolutional neural network (CNN) model, is trained on a high-resolution (HR) image dataset. In this paper, we analyze the behavior of applying a classical super-resolution (SR) method such as bicubic interpolation, and a deep CNN model such as SRCNN to enhance low-resolution (LR) weeds images used for classification. Using an HR dataset, we first train a CNN model for weeds image classification with a default input size of 128 × 128. Then, given an LR weeds image, we rescale to default input size by applying the bicubic interpolation or the SRCNN model. We analyze these two approaches on the Chonnam National University (CNU) weeds dataset and find that SRCNN is suitable for the image size is smaller than 80 × 80, while bicubic interpolation is convenient for a larger image.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.2
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• pp.412-434
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• 2023

This study discusses the high-order diffusion method in the wavelet domain. It aims to improve the edge protection capability of the high-order diffusion method using wavelet coefficients that can reflect image information. During the first step of the proposed diffusion method, the wavelet packet decomposition is a more refined decomposition method that can extract the texture and structure information of the image at different resolution levels. The high-frequency wavelet coefficients are then used to construct the edge detection function. Subsequently, because accurate wavelet coefficients can more accurately reflect the edges and details of the image information, by introducing the idea of state weight, a scheme for recovering wavelet coefficients is proposed. Finally, the edge detection function is constructed by the module of the wavelet coefficients to guide high-order diffusion, the denoised image is obtained. The experimental results showed that the method presented in this study improves the denoising ability of the high-order diffusion model, and the edge protection index (SSIM) outperforms the main methods, including the block matching and 3D collaborative filtering (BM3D) and the deep learning-based image processing methods. For images with rich textural details, the present method improves the clarity of the obtained images and the completeness of the edges, demonstrating its advantages in denoising and edge protection.

• Smart Media Journal
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• v.12 no.3
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• pp.30-37
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• 2023

Alzheimer's disease (AD) symptoms are being treated by early diagnosis, where we can only slow the symptoms and research is still undergoing. In consideration, using T1-weighted images several classification models are proposed in Machine learning to identify AD. In this paper, we consider the improvised feature selection, to reduce the complexity by using wrapping techniques and Restricted Boltzmann Machine (RBM). This present work used the subcortical and cortical features of 278 subjects from the ADNI dataset to identify AD and sMRI. Multi-class classification is used for the experiment i.e., AD, EMCI, LMCI, HC. The proposed feature selection consists of Forward feature selection, Backward feature selection, and Combined PCA & RBM. Forward and backward feature selection methods use an iterative method starting being no features in the forward feature selection and backward feature selection with all features included in the technique. PCA is used to reduce the dimensions and RBM is used to select the best feature without interpreting the features. We have compared the three models with PCA to analysis. The following experiment shows that combined PCA &RBM, and backward feature selection give the best accuracy with respective classification model RF i.e., 88.65, 88.56% respectively.

• Smart Media Journal
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• v.11 no.11
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• pp.32-39
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• 2022

As IoT applications that provide AI services increase, various hardware and software that support autonomous learning and inference are being developed. However, as the characteristics and constraints of each hardware increase difficulties in developing IoT applications, the development of an integrated platform is required. In this paper, we propose a tool for automatically generating components based on artificial neural networks and spiking neural networks as well as IoT technologies to be compatible with open platforms. The proposed component automatic generation tool supports the creation of components considering the characteristics of various hardware devices through the virtual component layer of IoT and AI and enables automatic application to open platforms.