• International Journal of Contents
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• 제5권1호
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• pp.51-55
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• 2009

In this paper, we present a structure of personalized e-Learning system to study for a test formalized by uniform multiple-choice using on/off line mixed estimations as is the case of Driver :s License Test in Korea. Using the system a candidate can study toward the license through the Internet (and/or mobile instruments) within the personalized concept based on IRT(item response theory). The system accurately estimates user's ability parameter and dynamically offers optimal evaluation problems and learning contents according to the estimated ability so that the user can take possession of the license in shorter time. In order to establish the personalized e-Learning concepts, we build up 3 databases and 2 agents in this system. Content DB maintains learning contents for studying toward the license as the shape of objects separated by concept-unit. Item-bank DB manages items with their parameters such as difficulties, discriminations, and guessing factors, which are firmly related to the learning contents in Content DB through the concept of object parameters. User profile DB maintains users' status information, item responses, and ability parameters. With these DB formations, Interface agent processes user ID, password, status information, and various queries generated by learners. In addition, it hooks up user's item response with Selection & Feedback agent. On the other hand, Selection & Feedback agent offers problems and content objects according to the corresponding user's ability parameter, and re-estimates the ability parameter to activate dynamic personalized learning situation and so forth.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.1716-1722
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• 2005

Fuzzy logic, neural network, fuzzy-neural network play an important as the key technology of linguistic modeling for intelligent control and decision in complex systems. The fuzzy-neural network (FNN) learning represents one of the most effective algorithms to build such linguistic models. This paper proposes bacteria foraging algorithm based optimal learning fuzzy-neural network (BA-FNN). The proposed learning scheme is the fuzzy-neural network structure which can handle linguistic knowledge as tuning membership function of fuzzy logic by bacteria foraging algorithm. The learning algorithm of the BA-FNN is composed of two phases. The first phase is to find the initial membership functions of the fuzzy neural network model. In the second phase, bacteria foraging algorithm is used for tuning of membership functions of the proposed model.

• ETRI Journal
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• 제41권6호
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• pp.750-759
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• 2019

Presently, the extraction of hand-crafted features is still the dominant method in radar emitter recognition. To solve the complicated problems of selection and updation of empirical features, we present a novel automatic feature extraction structure based on deep learning. In particular, a convolutional neural network (CNN) is adopted to extract high-level abstract representations from the time-frequency images of emitter signals. Thus, the redundant process of designing discriminative features can be avoided. Furthermore, to address the performance degradation of a single platform, we propose the construction of an ensemble learning-based architecture for multi-platform fusion recognition. Experimental results indicate that the proposed algorithms are feasible and effective, and they outperform other typical feature extraction and fusion recognition methods in terms of accuracy. Moreover, the proposed structure could be extended to other prevalent ensemble learning alternatives.

• 융합보안논문지
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• 제8권4호
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• pp.81-89
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• 2008

상황인지에 기반한 유비쿼터스 학습서비스 응용모형에 대하여 소개한다. 이동성과 자율성에 기반하여 피학습자에게 WPAN 환경하의 적절한 상황인지 맞춤형 컨텐츠를 제공하고, 학습관리모형을 통해 경험과 선호사항 그리고 후보추천이 서비스제공 시점에 동적으로 재구성되는 서비스 응용 매커니즘을 소개한다. 개방형모형으로써 OSGi 미들웨어를 적용하며, 상황정보 및 프로파일 관리를 위해 메타모형을 통한 동적 재구성 매카니즘이 소개된다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.22.2-22
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• 2001

Recently, Reinforcement Learning (RL) methods in MDP have been extended and applied to the POMDP problems. Currently, hierarchical RL methods are widely studied. However, they have the drawback that the learning time and memories are exhausted only for keeping the hierarchical structure, though they aren´t necessary. On the other hand, our "Labeling Q-learning (LQ-learning) proposed previously, has no hierarchical structure, but adopts a characteristic internal memory mechanism. Namely, LQ-1earning agent percepts the state by pair of observation and its label, and the agent can distinguish states, which look as same, but obviously different, more exactly. So to speak, at each step t, we define a new type of perception of its environment ～ot = (ot, $\theta$t), where of is conventional observation, and $\theta$t is the label attached to the observation. Then the conventional ...

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1993년도 Fifth International Fuzzy Systems Association World Congress 93
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• pp.1171-1174
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• 1993

A promising approach to get the benefits of neural networks and fuzzy logic is to combine them into an integrated system to merge the computational power of neural networks and the representation and reasoning properties of fuzzy logic. In this context, this paper presents a fuzzy neural network which is able to code fuzzy knowledge in the form of it-then rules in its structure. The network also provides an efficient structure not only to code knowledge, but also to support fuzzy reasoning and information processing. A learning scheme is also derived for a class of membership functions.

• 대한토목학회논문집
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• 제40권2호
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• pp.239-245
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• 2020

최근 BIM (Building Information Modeling)이 건설 산업계에서 폭넓게 활용되고 있다. 하지만 과거에 시공이 된 구조물에 경우 대부분 BIM이 구축되어 있지 않다. BIM이 구축되지 않은 구조물의 경우, 카메라로부터 얻은 2D 이미지에 SfM (Structure from Motion) 기법을 활용하면 3D 모델의 점군 데이터(Point cloud)를 생성하고 BIM을 구축할 수 있다. 하지만 이렇게 생성된 점군 데이터는 의미론적 정보가 포함되어 있지 않기 때문에, 수작업으로 구조물의 어떤 요소인지 분류해 주어야 한다. 따라서 본 연구에서는 구조물 구성요소를 분류하는 과정을 자동화하기 위하여 딥러닝을 적용하였다. 딥러닝 네트워크 구축에는 CNN (Convolutional Neural Network) 구조의 Inception-ResNet-v2를 사용하였고, 전이학습을 통하여 교량 구조물의 구성요소를 학습하였다. 개발된 시스템을 검증하기 위하여 수집한 데이터를 이용하여 구성요소를 분류한 결과, 교량의 구성요소를 96.13 %의 정확도로 분류할 수 있었다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권2호
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• pp.1043-1056
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• 2017

A styled document is a document that contains diverse decorating functions such as different font, colors, tables and images generally authored in a word processor (e.g., MS-WORD, Open Office). Compared to a plain-text document, a styled document enables a human to easily recognize a logical structure such as section, subsection and contents of a document. However, it is difficult for a computer to recognize the structure if a writer does not explicitly specify a type of an element by using the styling functions of a word processor. It is one of the obstacles to enhance document version management systems because they currently manage the document with a file as a unit, not the document elements as a management unit. This paper proposes a machine learning based approach to analyzing the logical structure of a styled document composing of sections, subsections and contents. We first suggest a feature vector for characterizing document elements from a styled document, composing of eight features such as font size, indentation and period, each of which is a frequently discovered item in a styled document. Then, we trained machine learning classifiers such as Random Forest and Support Vector Machine using the suggested feature vector. The trained classifiers are used to automatically identify logical structure of a styled document. Our experiment obtained 92.78% of precision and 94.02% of recall for analyzing the logical structure of 50 styled documents.

• 정보보호학회논문지
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• 제30권3호
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• pp.337-347
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• 2020

RYPTO 2019에 발표된 Gohr의 연구결과는 딥러닝 기술이 암호분석에 활용될 수 있음을 보여주었다. 본 논문에서는 특정 구조를 가진 S-box를 딥러닝 기술이 식별할 수 있는지 실험한 결과를 제시한다. 이를 위해, 2가지 실험을 수행하였다. 첫 번째로는, 경량암호 설계에 주로 사용하는 Feistel 및 MISTY, SPN, multiplicative inverse 구조를 가진 S-box의 DDT 및 LAT로 학습 데이터를 구성하고 딥러닝 알고리즘으로 구조를 식별하는 실험을 수행하여 구조는 물론 라운드까지 식별할 수 있었다. 두 번째로는 Feistel 및 MISTY 구조가 특정 라운드까지 의사난수성을 보이는지에 대한 실험을 통해 이론적으로 제시된 라운드 수 보다 많은 라운드 수에서 random한 함수와 구분할 수 있음을 확인하였다. 일반적으로, 군사용 등 고도의 기밀성 유지를 위해 사용되는 암호들은 공격이나 해독을 근본적으로 차단하기 위해 설계정보를 공개하지 않는 것이 원칙이다. 본 논문에서 제시된 방법은 딥러닝 기술이 이처럼 공개되지 않은 설계정보를 분석하는 하나의 도구로 사용 가능하다는 것을 보여준다.

• 한국농공학회논문집
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• 제61권3호
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• pp.55-65
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• 2019

Although crack on concrete exists from its early formation, crack requires attention as it affects stiffness of structure and can lead demolition of structure as it grows. Detecting cracks on concrete is needed to take action prior to performance degradation of structure, and deep learning can be utilized for it. In this study, transfer learning, one of the deep learning techniques, was used to detect the crack, as the amount of crack's image data was limited. Pre-trained Inception-v3 was applied as a base model for the transfer learning. Web scrapping was utilized to fetch images of concrete wall with or without crack from web. In the recognition of crack, image post-process including changing size or removing color were applied. In the visualization of crack, source images divided into 30px, 50px or 100px size were used as input data, and different numbers of input data per category were applied for each case. With the results of visualized crack image, false positive and false negative errors were examined. Highest accuracy for the recognizing crack was achieved when the source images were adjusted into 224px size under gray-scale. In visualization, the result using 50 data per category under 100px interval size showed the smallest error. With regard to the false positive error, the best result was obtained using 400 data per category, and regarding to the false negative error, the case using 50 data per category showed the best result.