• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 Proceedings of the Korea Automatic Control Conference, 11th (KACC); Pohang, Korea; 24-26 Oct. 1996
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• pp.303-306
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• 1996

We propose a robust tuning method of the quadratic criterion based iterative learning control(Q-ILC) algorithm for discrete-time linear batch system. First, we establish the frequency domain representation for batch systems. Next, a robust convergence condition is derived in the frequency domain. Based on this condition, we propose to optimize the weighting matrices such that the upper bound of the robustness measure is minimized. Through numerical simulation, it is shown that the designed learning filter restores robustness under significant model uncertainty.

• 한국수학교육학회지시리즈A:수학교육
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• 제58권3호
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• pp.403-422
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• 2019

2015 개정 중학교 1학년 수학 교과서 총 10종을 대상으로 분석틀에 근거하여 교과서 체제별로 그래프의 표현과 해석에 관한 요소를 빈도 분석하고 교차분석 하였으며, 그래프 내용에 관한 학생들의 만족도를 조사하였다. 그 결과, 전반적으로 교과서에 그래프의 표현보다 해석에 관한 문항이 더 많이 수록되어 있으며, 또 학생들은 그래프 단원에 학습 효과는 보였지만 해당 학습에 관한 감동 여부에는 중립적인 반응을 보였다.

• Smart Structures and Systems
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• 제29권1호
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• pp.93-103
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• 2022

With the wider availability of sensor technology through easily affordable sensor devices, several Structural Health Monitoring (SHM) systems are deployed to monitor vital civil infrastructure. The continuous monitoring provides valuable information about the health of the structure that can help provide a decision support system for retrofits and other structural modifications. However, when the sensors are exposed to harsh environmental conditions, the data measured by the SHM systems tend to be affected by multiple anomalies caused by faulty or broken sensors. Given a deluge of high-dimensional data collected continuously over time, research into using machine learning methods to detect anomalies are a topic of great interest to the SHM community. This paper contributes to this effort by proposing a relatively new time series representation named "Shapelet Transform" in combination with a Random Forest classifier to autonomously identify anomalies in SHM data. The shapelet transform is a unique time series representation based solely on the shape of the time series data. Considering the individual characteristics unique to every anomaly, the application of this transform yields a new shape-based feature representation that can be combined with any standard machine learning algorithm to detect anomalous data with no manual intervention. For the present study, the anomaly detection framework consists of three steps: identifying unique shapes from anomalous data, using these shapes to transform the SHM data into a local-shape space and training machine learning algorithms on this transformed data to identify anomalies. The efficacy of this method is demonstrated by the identification of anomalies in acceleration data from an SHM system installed on a long-span bridge in China. The results show that multiple data anomalies in SHM data can be automatically detected with high accuracy using the proposed method.

• 인터넷정보학회논문지
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• 제11권5호
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• pp.95-104
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• 2010

학습을 이해하는 새로운 패러다임으로 구성주의가 등장하면서 협력학습의 필요성이 강조되고 있다. 특히 교수와 학습에 대한 새로운 접근을 지원하는 테크놀로지로 온라인이 부각되면서 온라인 협력학습에 대한 관심이 증대하고 있다. 본 연구는 온라인 협력학습에서 하나의 주요한 요인인 학습과제 유형에 따른 협력학습 모형을 탐색하여 온라인 협력학습 실제에 도움을 주고자 하였다. 이를 위해 학습과제를 문제해결과제와 지식학습과제로 분류한 후, 학습과제 유형별로 적합한 온라인 협력학습 설계와 환경, 그리고 학습과정을 살펴보았다.

• 대한수학교육학회지:수학교육학연구
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• 제14권1호
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• pp.39-69
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• 2004

본 연구에서는 학생들이 일차함수의 활용단원을 학습할 때 여러 현상을 해석하고 다양한 수학적 표현을 사용하여 모델로 만들어 문제해결과정에 이를 적용할 수 있도록, 학생들의 표현에 대한 이전 경험과 현상을 해석하기 위한 표현 방법을 효과적으로 연결하는 학습-지도 방법을 분석하였다. 본 연구는 일차함수를 학습한 8학년 학생들을 대상으로 일차함수 단원을 예측과제, 번역과제, 해석과제, 척도과제로 세분화하여 각각에 대한 학생들의 오류를 분석한 다음, 일차함수의 활용 단원을 교과서 위주의 강의식 표현변환 학습, 모델링 관점에서의 표현변환 학습과 과제기반 표현변환 학습을 실시하였다. 연구 결과, 강의식 학습 방법보다는 모델링 관점과 과제기반 학습이 표현변환의 유연한 연결성 및 일차함수에 대한 각 과제별 오류교정과 질적 함수에 대한 해석 능력에서 효과적이었다. 모델링 관점과 과제기반 학습의 경우는 모두 표현변환의 유연한 연결을 교수하는데 효과적이었으나, 질적 함수의 해석 능력에서는 모델링 관점의 학습이 보다 효과적이었다.

• 한국멀티미디어학회논문지
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• 제23권8호
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• pp.965-976
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• 2020

We present in this work a segmentation method of E. coli bacterial images generated via phase contrast microscopy using a deep learning based hybrid feature generation. Unlike conventional machine learning methods that use the hand-crafted features, we adopt the denoising autoencoder in order to generate a precise and accurate representation of the pixels. We first construct a hybrid vector that combines original image, difference of Gaussians and image gradients. The created hybrid features are then given to a deep autoencoder that learns the pixels' internal dependencies and the cells' shape and boundary information. The latent representations learned by the autoencoder are used as the inputs of a softmax classification layer and the direct outputs from the classifier represent the coarse segmentation mask. Finally, the classifier's outputs are used as prior information for a graph partitioning based fine segmentation. We demonstrate that the proposed hybrid vector representation manages to preserve the global shape and boundary information of the cells, allowing to retrieve the majority of the cellular patterns without the need of any post-processing.

• Smart Structures and Systems
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• 제29권6호
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• pp.777-783
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• 2022

Even though several deep learning-based methods have been applied in the field of acoustic source localization, the previous works have only been conducted using the two-dimensional representation of the beamforming maps, particularly with the planar array system. While the acoustic sources are more required to be localized in a spherical microphone array system considering that we live and hear in the 3D world, the conventional 2D equirectangular map of the spherical beamforming map is highly vulnerable to the distortion that occurs when the 3D map is projected to the 2D space. In this study, a 3D deep learning approach is proposed to fulfill accurate source localization via distortion-free 3D representation. A target function is first proposed to obtain 3D source distribution maps that can represent multiple sources' positional and strength information. While the proposed target map expands the source localization task into a point-wise prediction task, a PointNet-based deep neural network is developed to precisely estimate the multiple sources' positions and strength information. While the proposed model's localization performance is evaluated, it is shown that the proposed method can achieve improved localization results from both quantitative and qualitative perspectives.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권3호
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• pp.670-684
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• 2024

3D Cross-Modal Retrieval (3DCMR) is a task that retrieves 3D objects regardless of modalities, such as images, meshes, and point clouds. One of the most prominent methods used for 3DCMR is the Cross-Modal Center Loss Function (CLF) which applies the conventional center loss strategy for 3D cross-modal search and retrieval. Since CLF is based on center loss, the center features in CLF are also susceptible to subtle changes in hyperparameters and external inferences. For instance, performance degradation is observed when the batch size is too small. Furthermore, the Mean Squared Error (MSE) used in CLF is unable to adapt to changes in batch size and is vulnerable to data variations that occur during actual inference due to the use of simple Euclidean distance between multi-modal features. To address the problems that arise from small batch training, we propose a Noisy Center Loss (NCL) method to estimate the optimal center features. In addition, we apply the simple Siamese representation learning method (SimSiam) during optimal center feature estimation to compare projected features, making the proposed method robust to changes in batch size and variations in data. As a result, the proposed approach demonstrates improved performance in ModelNet40 dataset compared to the conventional methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권4호
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• pp.2109-2123
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• 2017

This paper presents a super-resolution reconstruction method based on sparse representation for two-dimensional bar code images. Considering the features of two-dimensional bar code images, Kirsch and LBP (local binary pattern) operators are used to extract the edge gradient and texture features. Feature extraction is constituted based on these two features and additional two second-order derivatives. By joint dictionary learning of the low-resolution and high-resolution image patch pairs, the sparse representation of corresponding patches is the same. In addition, the global constraint is exerted on the initial estimation of high-resolution image which makes the reconstructed result closer to the real one. The experimental results demonstrate the effectiveness of the proposed algorithm for two-dimensional bar code images by comparing with other reconstruction algorithms.

• 정보교육학회논문지
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• 제20권1호
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• pp.13-20
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• 2016

실세계에서 데이터는 숫자와 문자, 이미지, 소리 등 다양한 형태로 존재한다. 하지만 컴퓨터에서는 실세계의 데이터를 디지털 형태인 1과 0으로 표현하고 있다. 초등학생들은 전통적인 강의식 방법으로는 컴퓨터에서 사용하고 있는 데이터 표현에 대한 개념을 이해하기에는 매우 어렵다. 따라서 본 논문에서는 초등학생들이 데이터 표현 개념을 쉽게 이해할 수 있도록 기존의 연구를 분석하고 언플러그드 교수-학습 방법에 대해 연구하였다. 이를 위해 정보교육학회 소프트웨어교육 내용 체계를 분석하고, 초등학교 중학년을 위한 데이터 표현 교육 내용을 선정하였다. 또한 데이터 표현 교육을 위해 언플러그드 방식을 이용한 교수-학습 지도안을 개발하고 수업자료를 개발하였다. 본 연구에서 제시한 언플러그드를 통한 교수-학습 방법이 데이터 표현 교육에 도움이 되기를 기대한다.