• Journal of the Korean Institute of Telematics and Electronics
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• v.26 no.6
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• pp.108-114
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• 1989

Introducing and optimizing bit-significance to the Hopfield model, ten highly correlated binary images, i.e., numbers "0" to "9", are successfully stored and retrieved in a $6{}8$ node system. Unlike many other neural network models, this model has stronger error correction capability for correlated images such as "6","8","3", and "9". The bit significance optimization is regarded as an adaptive learning process based on least-mean-square error algorithm, and may be implemented with Widrow-Hoff neural nets optimizer. A design for electro-optic implementation including the adaptive optimization networks is also introduced.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.5 s.305
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• pp.55-62
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• 2005

In this paper, we propose the face recognition system using HNMA(Hippocampal Neuron Modeling Algorithm) which can remodel the cerebral cortex and hippocampal neuron as a principle of a man's brain in engineering, then it can learn the feature-vector of the face images very fast and construct the optimized feature each image. The system is composed of two parts. One is feature-extraction and the other is teaming and recognition. In the feature extraction part, it can construct good-classified features applying PCA(Principal Component Analysis) and LDA(Linear Discriminants Analysis) in order. In the learning part, it cm table the features of the image data which are inputted according to the order of hippocampal neuron structure to reaction-pattern according to the adjustment of a good impression in the dentate gyrus region and remove the noise through the associate memory in the CA3 region. In the CA1 region receiving the information of the CA3, it can make long-term memory learned by neuron. Experiments confirm the each recognition rate, that are face changes, pose changes and low quality image. The experimental results show that we can compare a feature extraction and learning method proposed in this paper of any other methods, and we can confirm that the proposed method is superior to existing methods.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.10
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• pp.2087-2092
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• 2011

In this paper, we use the earth's magnetic field is measured by the MR device. By analyzing the measured data to determine the direction to implement the system. The construction of the system to determine the direction of neural networks, using input data based on an analysis of the relationship between pattern formation and characteristics of study and related information through a pattern when it is remembered that the output feedback input to associative networks and proves its feasibility for implementation. The entire detection system with regional changes in the Earth's magnetic field to adapt to the environment should aim to build a system.

• The KIPS Transactions:PartD
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• v.16D no.1
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• pp.139-144
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• 2009

Neural network is a very exciting and generic framework to develop almost all ranges of machine learning technologies and its potential is far beyond its current capabilities. Among other characteristics, neural network acts as associative memory obtained from the values structurally stored in synaptic inherent structure. Due to innate complexity of neural networks system, in its practical implementation and maintenance, multifaceted problems are known to be unavoidable. In this paper, we present design and implementation details of GUI software which can be valuable tool to maintain and develop neural networks. It has capability of displaying every state of synaptic weights with network nodal relation in each learning step.

• Journal of the Korea Convergence Society
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• v.11 no.8
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• pp.99-106
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• 2020

The purpose of this study is to find out whether image similarity between celebrities and brands on social network service be a determinant to predict advertising effectiveness. To this end, an advertising effect prediction model for celebrity endorsed advertising was created and its validity was verified through a machine learning method which is a big data analysis technique. Firstly, the celebrity-brand image similarity, which was used as an independent variable, was quantified by the association network theory with social big data, and secondly a multiple regression model which used data representing advertising effects as a dependent variable was repeatedly conducted to generate an advertising effect prediction model. The accuracy of the prediction model was decided by comparing the prediction results with the survey outcomes. As for a result, it was proved that the validity of the predictive modeling of advertising effects was secured since the classification accuracy of 75%, which is a criterion for judging validity, was shown. This study suggested a new methodological alternative and direction for big data-based modeling research through celebrity-brand image similarity structure based on social network theory, and effect prediction modeling by machine learning.

• Journal of The Korean Association For Science Education
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• v.24 no.5
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• pp.869-885
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• 2004

The aim of this study is to acquire a better understanding of how natural history(NH) and natural history museums(NHM) are perceived among students, teachers and parents by administering a questionnaire. A total of over 2500 people participated in this survey. We developed the questionnaire which was categorized by three specific areas: 1) people's overall perceptions of natural history and natural history museums; 2)the way of people's getting such perceptions and knowledge; 3) people's possessing interests in NH and NHM. The summary of survey results included; firstly people had paid attention to nature and natural environments; secondly NH and NHM were not familiar to people, but they expressed their eagerness to learn about them; thirdly the first image occurred to people was dinosaur, when they recalled NH and NHM with describing them to be mysterious and education.; on the fourth, NH and NHM were informed through TV programs; lastly, most people responded that they never learned about NH and NHM in schools but they wanted to learn them.

• Journal of Korean Elementary Science Education
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• v.32 no.4
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• pp.545-556
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• 2013

The purpose of this research was to enhance the understanding of scientific thinking of $5^{th}$ graders of elementary school through conducting investigative analyses on the students' associations with regard to illustrations on science textbooks and to provide basic data that are needed for the teachers teaching science in classrooms to reorganize textbook illustrations suitable to the realities of science contents and realms. In order to achieve the research purposes, subject $5^{th}$ grade students were asked to write down what they associated with the illustrations on the matter part of the $5^{th}$ science textbooks of elementary school: among 14 illustrations, a half of them were after lessons and other half from before lessons. The types of students' learning goal statements according to Bloom's taxonomy of educational objectives were compared with the learning objectives provided in teachers' guide. The differences between before and after lessons in associated words which students used responding to given illustrations were investigated. Students' responses were analyzed in terms of how their associations were consistent with what their preference of learning objectives would be as well. Students variables including their achievement levels and gender were used as group variables in order to locate their effects on differences in their associations before and after the lessons. It was found that students manipulated the given illustrations more variously with more explanations before the lessons than after. After the lessons students tended to describe the illustrations more homogeneously and made theirs stick on the given direction by the textbooks. The implications for how school teaching affected to students' perception was made.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.5
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• pp.959-966
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• 2017

Currently, the market of digital contents such as e-books, cartoons and webtoons is growing up, but the copyrights infringement are serious issue due to their distribution through illegal ways. However, the technologies for copyright protection are not developed enough. Therefore, in this paper, we propose the NSS-based publication classification method for copyright protection. Using histogram calculated by NSS, we propose classification method for digital contents using SVM. The proposed algorithm will be useful for copyright protection because it lets us distinguish illegal distributed digital contents more easily.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.2
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• pp.313-320
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• 2023

The cumulative registration status of dogs is from 1.07 million in 2016 to 2.32 million in 2020. Animal registration is increasing by more than 10% every year, and accordingly, a name must be decided when registering a dog. We want to give a name that fits the characteristics of a dog's appearance, but there are many difficulties in naming it. This paper explains the development of a system for recognizing dog images and recommends dog names based on similar objects or food. This system extracts similarities with dogs' images through models that learn images of various objects and foods, and recommends dog names based on similarities. In addition, by recommending additional related words based on the image data of the result value, it was possible to provide users with various options, increase convenience, and increase interest and fun. Through this system, it is expected that users will be able to solve their concerns about naming their dogs, check names that suit their dogs comfortably, and give them various options through various recommended names to increase satisfaction.

• Proceedings of the Korea Water Resources Association Conference
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• 2005.05b
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• pp.1068-1072
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• 2005

조석이란, 해면의 완만한 주기적 승강을 말하며, 보통 그 승강은 1일 약 2회이나, 곳에 따라서는 1일 1회의 곳도 있다. 조석에 있어서는 이 밖에 수일의 주기를 갖는 약간 불규칙한 승강, 반년, 또는 1년을 주기로 하는 다소 규칙적인 승강까지 포함하여 취급한다. 그러나, 각 항만마다 갖는 특정적인 주기인 수분내지 수십분의 주기의 승강은 조석으로 취급하지 않는다. 조석은 해양의 제현상 중에서 예측가능성이 가장 큰 현장으로 이는 조석이 천체의 운행과 연관되기 때문이다. 조석이란 지구로부터 일정한 거리에서 각 고유의 속도를 가지는 적도상을 운행하는 무수의 가상천체에 기인하는 규칙적인 개개의 조석을 합성한 것이며 이 개개의 조석을 분조(Constituent)라 한다. 여기에서 사용되는 신경망 모형은 입력과 출력으로 구성되는 블랙박스 모형으로서 하나의 시스템을 병렬적으로 비선형적으로 구축할 수 있다는 장점 때문에 과거 하천유역의 강우-유출과정에서의 경우 유출현상을 해석하고 유출과정을 모형화 하기 위해 사용하였다. 본 연구에서는 기존의 조위 예측방법인 조화분석법이 아닌 인공신경망을 이용하여 조위예측을 실시하였다. 학습이라는 최적화 과정을 통해 구조와 기능이 복잡한 자연현상을 그대로 받아들여 축적시킴으로써 이를 지식으로 현상에 대한 재현능력이 뛰어나고, 또한 신경회로망의 연상기억능력에 적용하여 수학적으로 표현이 불가능한 불확실한 조위곡선에 적용하기에 유리한 장점을 가지고 있다. 본 연구의 목적은 과거 조위이론을 통해 이루었던 조위예측을 우리가 알기 쉬운 여러 기후인자(해면기압, 풍향, 풍속, 음력 등)에 따른 조위곡선을 예측하기 위해 신경망 모형을 이용하여 여수지역의 조위에 적용하여 비교 분석하고자 한다. May가 제안한 공식을 더 확장하여 적용할 수 있는 실험 공식으로 개선하였으며 다양한 조건에 대한 실험을 수행하여 보다 정밀한 공식으로 개선할 수 있었다.$10,924m^3/s$ 및 $10,075m^3/s$로서 실험 I의 $2,757m^3/s$에 비해 통수능이 많이 개선되었음을 알 수 있다.함을 알 수 있다. 상수관로 설계 기준에서는 관로내 수압을 $1.5\~4.0kg/cm^2$으로 나타내고 있는데 $6kg/cm^2$보다 과수압을 나타내는 경우가 $100\%$로 밸브를 개방하였을 때보다 $60\%,\;80\%$ 개방하였을 때가 더 빈번히 발생하고 있으므로 대상지역의 밸브 개폐는 $100\%$ 개방하는 것이 선계기준에 적합한 것으로 나타났다. 밸브 개폐에 따른 수압 변화를 모의한 결과 밸브 개폐도를 적절히 유지하여 필요수량의 확보 및 누수방지대책에 활용할 수 있을 것으로 판단된다.8R(mm)(r^2=0.84)$로 지수적으로 증가하는 경향을 나타내었다. 유거수량은 토성별로 양토를 1.0으로 기준할 때 사양토가 0.86으로 가장 작았고, 식양토 1.09, 식토 1.15로 평가되어 침투수에 비해 토성별 차이가 크게 나타났다. 이는 토성이 세립질일 수록 유거수의 저항이 작기 때문으로 생각된다. 경사에 따라서는 경사도가 증가할수록 증가하였으며 $10\% 경사일 때를 기준으로 $Ro(mm)=Ro_{10}{\times}0.797{\times}e^{-0.021s(\%)}$로 나타났다.천성 승모판 폐쇄 부전등을 초래하는 심각한 선