• 한국IT서비스학회지
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• 제13권1호
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• pp.197-207
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• 2014

With the recent advance in smartphones, users are allowed to use mobile applications anytime anywhere, and change their way to interact with smart environment and people. As a result, the need for developing context-aware applications on smartphones has a great attention from users and developers. This paper proposes a context-aware framework for supporting UI/UX of smartphones. The proposed framework collects a wide range of sensory data from smartphones and allows developers to analyze and model context models for their desired apps. In addition, it also supports real-time inference within the apps to make them to adapt to context. In order to show effectiveness of the proposed framework, we introduce two smartphone apps: context-aware home screen and automatic detection of smartphone problem use. Therefore, we expect that the proposed framework will help developers easily implement their apps with respect to context-awareness.

• 지능정보연구
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• 제19권2호
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• pp.177-189
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• 2013

스마트교육에서 디지털교과서의 역할은 학습자와 대면하는 교육미디어로써 그 중요성은 재론의 여지없다. 이러한 디지털교과서는 학습자의 편의와 더불어 교수자, 콘텐츠 제작자, 유통업자를 위하여 표준화되어야 활성화되고 산업화될 수 있다. 본 연구에서는 다음과 같은 3가지 목표를 지향하는 디지털교과서 표준화 방안을 모색한다. (1) 디지털교과서는 온-오프 수업을 모두 지원하는 혼합학습 매체의 역할을 해야 하며, 특별한 전용뷰어 없이 표준을 준수하는 모든 EPUB 뷰어에서 실행가능 해야 하며, 기존의 이러닝 학습 콘텐츠와 학습관리시스템를 활용할 수 있도록 하며, 디지털 교과서를 사용하는 학습자의 정보를 추적 관리할 수 있는 트랙킹기능이 있으면서도, 오프라인 동안의 정보를 축적하여 서버와 통신할 수 있는 기능도 필요하다. 디지털교과서의 표준으로서 EPUB을 고려하는 이유는 디지털교과서가 책의 형태를 가져야 하는데 이를 위해서 따로 표준을 정할 필요가 없으며, EPUB 표준을 채택함으로써 풍부한 콘텐츠, 유통구조, 산업기반을 활용할 수 있기 때문이다. (2) 디지털교과서는 오픈소스를 적극 활용하여 저비용으로 현재 사용가능한 서비스를 구성하여 표준과 더불어 실제 실행 가능한 프로그램으로 제시되어야 하며, 관련 학습 콘텐츠가 오픈마켓의 형태로 운영될 수 있어야 한다. (3) 디지털교과서는 학습자에게 적절한 학습 피드백을 제공하기 위하여 모든 학습활동 정보를 축적하고 관리될 수 있는 인프라를 표준에 따라 구축하여 교육 빅데이터 처리의 기반을 제공하여야 한다. 이북 표준인 EPUB 3.0을 기반으로 하는 오픈 디지털교과서는 (1) 학습활동 정보를 기록하고 (2) 이 학습활동 지원을 위한 서버와 통신하여야 한다. 현재 표준으로 정해져 있지 않은 이북의 기록과 통신 기능을 EPUB 3.0의 JavaScript로 구현하여 현재 EPUB 3.0 뷰어에서도 활용하면서 이를 차세대 이북 표준 또는 교육을 위한 이북 표준(EPUB 3.0 for education)으로 제안하여 향후 제정된 표준 이북 뷰어에서는 JavaScript없이도 처리되도록 하는 전략이 필요하다. 향후 연구는 제안한 오픈 디지털교과서 표준에 의한 오픈소스 프로그램을 개발하고, 개발된 오픈 디지털교과서의 학습활동정보를 활용한 새로운 교육서비스 방안(교육 빅데이터 활용방안 포함)을 제시하는 것이다.

• International journal of advanced smart convergence
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• 제11권2호
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• pp.13-21
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• 2022

In this paper, we propose a learning based joint demosaicing and super-resolution framework which uses only the mosaiced color filter array(CFA) image as the input. As the proposed method works only on the mosaicied CFA image itself, there is no need for a large dataset. Based on our framework, we proposed two different structures, where the first structure uses one deep image prior network, while the second uses two. Experimental results show that even though we use only the CFA image as the training image, the proposed method can result in better visual quality than other bilinear interpolation combined demosaicing methods, and therefore, opens up a new research area for joint demosaicing and super-resolution on raw images.

• Smart Structures and Systems
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• 제32권1호
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• pp.61-81
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• 2023

This study presents an ensemble learning based Bayesian model updating approach for structural damage diagnosis. In the developed framework, the structure is initially decomposed into a set of substructures. The autoregressive moving average (ARMAX) model is established first for structural damage localization based structural motion equation. The wavelet packet decomposition is utilized to extract the damage-sensitive node energy in different frequency bands for constructing structural surrogate models. Four methods, including Kriging predictor (KRG), radial basis function neural network (RBFNN), support vector regression (SVR), and multivariate adaptive regression splines (MARS), are selected as candidate structural surrogate models. These models are then resampled by bootstrapping and combined to obtain an ensemble model by probabilistic ensemble. Meanwhile, the maximum entropy principal is adopted to search for new design points for sample space updating, yielding a more robust ensemble model. Through the iterations, a framework of surrogate ensemble learning based model updating with high model construction efficiency and accuracy is proposed. The specificities of the method are discussed and investigated in a case study.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권6호
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• pp.2168-2187
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• 2021

The smart grid replaces the traditional power structure with information inventiveness that contributes to a new physical structure. In such a field, malicious information injection can potentially lead to extreme results. Incorrect, FDI attacks will never be identified by typical residual techniques for false data identification. Most of the work on the detection of FDI attacks is based on the linearized power system model DC and does not detect attacks from the AC model. Also, the overwhelming majority of current FDIA recognition approaches focus on FDIA, whilst significant injection location data cannot be achieved. Building on the continuous developments in deep learning, we propose a Deep Learning based Locational Detection technique to continuously recognize the specific areas of FDIA. In the development area solver gap happiness is a False Data Detector (FDD) that incorporates a Convolutional Neural Network (CNN). The FDD is established enough to catch the fake information. As a multi-label classifier, the following CNN is utilized to evaluate the irregularity and cooccurrence dependency of power flow calculations due to the possible attacks. There are no earlier statistical assumptions in the architecture proposed, as they are "model-free." It is also "cost-accommodating" since it does not alter the current FDD framework and it is only several microseconds on a household computer during the identification procedure. We have shown that ANN-MLP, SVM-RBF, and CNN can conduct locational detection under different noise and attack circumstances through broad experience in IEEE 14, 30, 57, and 118 bus systems. Moreover, the multi-name classification method used successfully improves the precision of the present identification.

• International journal of advanced smart convergence
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• 제8권4호
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• pp.68-74
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• 2019

Along with the deeper architecture in the deep learning approaches, the need for the data becomes very big. In the real problem, to get huge data in some disciplines is very costly. Therefore, learning on limited data in the recent years turns to be a very appealing area. Meta-learning offers a new perspective to learn a model with this limitation. A state-of-the-art model that is made using a meta-learning framework, Meta-SGD, is proposed with a key idea of learning a hyperparameter or a learning rate of the fast adaptation stage in the outer update. However, this learning rate usually is set to be very small. In consequence, the objective function of SGD will give a little improvement to our weight parameters. In other words, the prior is being a key value of getting a good adaptation. As a goal of meta-learning approaches, learning using a single gradient step in the inner update may lead to a bad performance. Especially if the prior that we use is far from the expected one, or it works in the opposite way that it is very effective to adapt the model. By this reason, we propose to add a weight term to decrease, or increase in some conditions, the effect of this prior. The experiment on few-shot learning shows that emphasizing or weakening the prior can give better performance than using its original value.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권8호
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• pp.2831-2845
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• 2022

Compared to the significant approach of project-based learning research, a data-driven design project-based learning has not reached a meaningful consensus regarding the most valid and reliable method for assessing design creativity. This article proposes an advanced information data-interactive learning system for creative design using a service design process that combines a design thinking. We propose a service framework to improve the convergence design process between students and advanced information data analysis, allowing students to participate actively in the data visualization and research using patent data. Solving a design problem by discovery and interpretation process, the Advanced information-interactive learning framework allows the students to verify the creative idea values or to ideate new factors and the associated various feasible solutions. The student can perform the patent data according to a business intelligence platform. Most of the new ideas for solving design projects are evaluated through complete patent data analysis and visualization in the beginning of the service design process. In this article, we propose to adapt advanced information data to educate the service design process, allowing the students to evaluate their own idea and define the problems iteratively until satisfaction. Quantitative evaluation results have shown that the advanced information data-driven learning system approach can improve the design project - based learning results in terms of design creativity. Our findings can contribute to data-driven project-based learning for advanced information data that play a crucial role in convergence design in related standards and other smart educational fields that are linked.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2021년도 춘계학술대회
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• pp.505-507
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• 2021

영상 인식 기술은 인공지능 기술을 기반으로 인식하고자하는 객체의 형상, 객체 주변의 환경변화 및 객체 회전에 의한 인식 능력 저하를 보완할 수 있는 객체특징점 및 특징 기술자를 생성하고, 생성된 특징 기술자를 이용하여, 영상 객체를 인식하는 기술로, 일반적으로 영상에 나타난 객체를 인식하는 기술을 뜻한다. 스마트 안전 축사에서 전력소비 및 화재 발생 복합 환경 분석을 위해 설치되는 전력화재 관리 디바이스를 통합 관리함으로써 축사 전력 사용의 효율성 향상 및 전기 사용의 과부화로 발생할 수 있는 사고를 방지하여 축산 농가의 이익 증대 및 피해를 최소화하고 안전하고 최적화된 지능형 스마트 안전 축사를 개발하여 보급하는데 요구되는 전력 관리 프레임워크를 구현하는데 목적이 있다.

• 스마트미디어저널
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• 제10권2호
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• pp.22-29
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• 2021

In the past decade, deep learning has been applied to various medical image analysis tasks. Skeletal bone age estimation is clinically important as it can help prevent age-related illness and pave the way for new anti-aging therapies. Recent research has applied deep learning techniques to the task of bone age assessment and achieved positive results. In this paper, we propose a bone age prediction method using a deep convolutional neural network. Specifically, we first train a classification model that automatically localizes the most discriminative region of an image and crops it from the original image. The regions of interest are then used as input for a regression model to estimate the age of the patient. The experiments are conducted on a whole-body scintigraphy dataset that was collected by Chonnam National University Hwasun Hospital. The experimental results illustrate the potential of our proposed method, which has a mean absolute error of 3.35 years. Our proposed framework can be used as a robust supporting tool for clinicians to prevent age-related diseases.

• 스마트미디어저널
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• 제12권1호
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• pp.32-46
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• 2023

Natural Language Processing (NLP) has grown tremendously in recent years. Typically, bilingual, and multilingual translation models have been deployed widely in machine translation and gained vast attention from the research community. On the contrary, few studies have focused on translating between spoken and sign languages, especially non-English languages. Prior works on Sign Language Translation (SLT) have shown that a mid-level sign gloss representation enhances translation performance. Therefore, this study presents a new large-scale Korean sign language dataset, the Museum-Commentary Korean Sign Gloss (MCKSG) dataset, including 3828 pairs of Korean sentences and their corresponding sign glosses used in Museum-Commentary contexts. In addition, we propose a translation framework based on self-supervised learning, where the pretext task is a text-to-text from a Korean sentence to its back-translation versions, then the pre-trained network will be fine-tuned on the MCKSG dataset. Using self-supervised learning help to overcome the drawback of a shortage of sign language data. Through experimental results, our proposed model outperforms a baseline BERT model by 6.22%.