• Journal of Information Processing Systems
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• 제13권1호
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• pp.57-67
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• 2017

In this paper, Atanassov's intuitionistic fuzzy set theory is used to handle the uncertainty of students' knowledgeon domain concepts in an E-learning system. Their knowledge on these domain concepts has been collected from tests that were conducted during their learning phase. Atanassov's intuitionistic fuzzy user model is proposed to deal with vagueness in the user's knowledge description in domain concepts. The user model uses Atanassov's intuitionistic fuzzy sets for knowledge representation and linguistic rules for updating the user model. The scores obtained by each student were collected in this model and the decision about the students' knowledge acquisition for each concept whether completely learned, completely known, partially known or completely unknown were placed into the information table. Finally, it has been found that the proposed scheme is more appropriate than the fuzzy scheme.

• 한국과학교육학회지
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• 제39권2호
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• pp.295-305
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• 2019

본 연구와 관련된 선행 연구에서는 과학 교수 학습 과정에서 효과적인 시각적 표상 활용과 연구를 촉진하기 위한 목적으로 2개 차원으로 구성된 시각적 표상 능력의 교육목표 분류체계(visual representation competence taxonomy: VRC-T)가 개발되었다. 본 연구에서는 이러한 VRC-T에 기초하여 그림자 현상에 대한 초등학생의 시각적 표상 능력을 조사하고 그림자에 대한 과학 지식과 표상 능력 사이의 관계 및 VRC-T 인지 과정의 위계 관계를 탐색하고자 하였다. 연구 결과 그림자 현상에 대한 초등학생의 시각적 표상 능력을 '해석하기', '통합하기', '구성하기'의 대범주로 나누어 보면 대체적으로 '해석하기'가 가장 점수가 높고, 다음이 '구성하기', '통합하기'의 순으로 나타났다. 또 학생들이 정규 교육과정에서 그림자 관련 단원을 학습한 이후임에도 불구하고 시각적 표상 능력은 높지 않은 것으로 나타났다. 한편 텍스트 기반의 과학 지식은 시각적 표상 능력의 모든 범주와 상관이 높지 않았다. 이것은 텍스트 형식의 과학 지식을 가지고 있더라도 시각적 표상 능력은 갖추어져 있지 않을 가능성이 크다는 것과 과학 수업에서 시각적 표상을 좀 더 강조하여 다루어야 할 필요성을 나타낸다. 마지막으로 서열화 이론에 따라 그림자 현상에 대한 시각적 표상 능력의 인지 과정 위계 관계를 탐색한 결과, 인정비율을 다소 느슨하게 하는 경우 6개 인지 과정 사이에 일직선의 위계 관계가 발견되었다. 이것은 평가 도구나 과제, 시각적 표상 능력을 지도하는 수업 활동을 계획할 때 VRC-T가 유용하게 활용될 수 있는 분석틀임을 시사한다.

• 로봇학회논문지
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• 제15권2호
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• pp.197-204
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• 2020

In this paper, we present a learning platform for robotic grasping in real world, in which actor-critic deep reinforcement learning is employed to directly learn the grasping skill from raw image pixels and rarely observed rewards. This is a challenging task because existing algorithms based on deep reinforcement learning require an extensive number of training data or massive computational cost so that they cannot be affordable in real world settings. To address this problems, the proposed learning platform basically consists of two training phases; a learning phase in simulator and subsequent learning in real world. Here, main processing blocks in the platform are extraction of latent vector based on state representation learning and disentanglement of a raw image, generation of adapted synthetic image using generative adversarial networks, and object detection and arm segmentation for the disentanglement. We demonstrate the effectiveness of this approach in a real environment.

• 한국동물학회:학술대회논문집
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• 한국동물학회 1996년도 한국생물과학협회 국제학술대회
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• pp.271.1-271
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• 1996

No Abstract, See Full Text

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제9권6호
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• pp.187-194
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• 2020

본 논문은 클라이언트의 익명성과 개인 정보를 보장하는 토르 네트워크에서 앙상블 학습을 이용한 웹사이트 핑거프린팅 방법을 제안한다. 토르네트워크에서 수집된 트래픽 패킷들로부터 웹사이트 핑거프린팅을 위한 훈련 문제를 구성하며, 트리 기반 앙상블 모델을 적용한 웹사이트 핑거프린팅 시스템의 성능을 비교한다. 훈련 특징 벡터는 트래픽 시퀀스에서 추출된 범용 정보, 버스트, 셀 시퀀스 길이, 그리고 셀 순서로부터 준비하며, 각 웹사이트의 특징은 고정 길이로 표현된다. 실험 평가를 위해 웹사이트 핑거프린팅의 사용에 따른 4가지 학습 문제(Wang14, BW, CW_T, CW_H)를 정의하고, CUMUL 특징 벡터를 사용한 지지 벡터 기계 모델과 성능을 비교한다. 실험 평가에서, BW 경우를 제외하고 제안하는 통계 기반 훈련 특징 표현이 CUMUL 특징 표현보다 우수하다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.1981-1986
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• 2004

In this study, the basic motor control system had been investigated. The controller for this study consists of two main parts, a feedforward controller part and a feedback controller part. Each part will deals with different control problems. The feedback controller deals with robustness and stability, while the feedforward controller deals with response speed. The feedforward controller, used to solve the tracking control problem, is adaptable. To make such a tracking perfect, an adaptive law based on Feedback Error Learning (FEL) is designed so that the feedforward controller becomes an inverse system of the controlled plant. The novelty of FEL method lies in its use of feedback error as a teaching signal for learning the inverse model. The theory in $H^{\infty}$-Control is selected to be applied in the feedback part to guarantee the stability and solve the robust stabilization problems. The simulation of each individual part and the integrated one are taken to clarify the study.

• Journal of Information Processing Systems
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• 제16권2호
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• pp.239-245
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• 2020

Blockchain, machine learning, and big data are among the key components of the future IT track. These technologies are used in various fields; hence their increasing application. This paper discusses the technologies developed in various research fields, such as data representation, Blockchain application, 3D shape recognition and classification, query method, classification method, and search algorithm, to provide insights into the future paradigm. In this paper, we present a summary of 18 high-quality accepted articles following a rigorous review process in the fields of Blockchain, machine learning, and big data.

• 반도체디스플레이기술학회지
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• 제19권2호
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• pp.88-91
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• 2020

Image segmentation is the task of partitioning an image into multiple sets of pixels based on some characteristics. The objective is to simplify the image into a representation that is more meaningful and easier to analyze. In this paper, we apply deep-learning to pre-train the learning model, and implement an algorithm that performs image segmentation in real time by extracting frames for the stream input from the Android device. Based on the open source of DeepLab-v3+ implemented in Tensorflow, some convolution filters are modified to improve real-time operation on the Android platform.

• Journal of Information Processing Systems
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• 제17권2호
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• pp.411-425
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• 2021

In this paper, a vehicle recognition algorithm based on deep convolutional neural network and compression dictionary is proposed. Firstly, the network structure of fine vehicle recognition based on convolutional neural network is introduced. Then, a vehicle recognition system based on multi-scale pyramid convolutional neural network is constructed. The contribution of different networks to the recognition results is adjusted by the adaptive fusion method that adjusts the network according to the recognition accuracy of a single network. The proportion of output in the network output of the entire multiscale network. Then, the compressed dictionary learning and the data dimension reduction are carried out using the effective block structure method combined with very sparse random projection matrix, which solves the computational complexity caused by high-dimensional features and shortens the dictionary learning time. Finally, the sparse representation classification method is used to realize vehicle type recognition. The experimental results show that the detection effect of the proposed algorithm is stable in sunny, cloudy and rainy weather, and it has strong adaptability to typical application scenarios such as occlusion and blurring, with an average recognition rate of more than 95%.

• ETRI Journal
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• 제41권4호
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• pp.483-493
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• 2019

Considering the high dimensions of video sequences, it is often challenging to acquire a sufficient dataset to train the tracking models. From this perspective, we propose to revisit the idea of hand-crafted feature learning to avoid such a requirement from a dataset. The proposed tracking approach is composed of two phases, detection and tracking, according to how severely the appearance of a target changes. The detection phase addresses severe and rapid variations by learning a new appearance model that classifies the pixels into foreground (or target) and background. We further combine the raw pixel features of the color intensity and spatial location with convolutional feature activations for robust target representation. The tracking phase tracks a target by searching for frame regions where the best pixel-level agreement to the model learned from the detection phase is achieved. Our two-phase approach results in efficient and accurate tracking, outperforming recent methods in various challenging cases of target appearance changes.