• Proceedings of the Korean Society of Computer Information Conference
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• 2022.01a
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• pp.389-390
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• 2022

본 논문에서는 동물 이미지 분류를 위한 작은 데이터 세트를 기반으로 하는 향상된 딥 러닝 방법을 제안한다. 먼저, CNN을 사용하여 작은 데이터 세트에 대한 훈련 모델을 구축한다. 데이터 증대를 사용하여 훈련 세트의 데이터 샘플을 확장한다. 다음으로, VGG16과 같은 대규모 데이터 세트에서 사전 훈련된 네트워크를 사용하여 작은 데이터 세트의 병목 현상 기능을 추출한다. 그리하여 두 개의 NumPy 파일에 새로운 훈련 데이터 세트 및 테스트 데이터 세트로 저장한다. 마지막으로 완전히 연결된 네트워크를 훈련시킨다.

• Journal of the Korean Society for Nondestructive Testing
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• v.32 no.5
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• pp.518-525
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• 2012

In this study, the feasibility of hyperspectral reflectance imaging technique was investigated for the discrimination of viable and non-viable lettuce seeds. The spectral data of hyperspectral reflectance images with the spectral range between 750 nm and 1000 nm were used to develop PLS-DA model for the classification of viable and non-viable lettuce seeds. The discrimination accuracy of the calibration set was 81.6% and that of the test set was 81.2%. The image analysis method was developed to construct the discriminant images of non-viable seeds with the developed PLS-DA model. The discrimination accuracy obtained from the resultant image were 91%, which showed the feasibility of hyperspectral reflectance imaging technique for the mass discrimination of non-viable lettuce seeds from viable ones.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.701-702
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• 2021

The purpose of the study was to identify fiber changes induced by electrical stimulation of a certain neural substrate in the rat brain. In the stimulation group, the peripheral nerve was injured and the brain area associated to inhibit sensory information was electrically stimulated. There were sham and sham stimulation groups as controls. Then high-field diffusion tensor imaging (DTI) was acquired. 35 features were taken from the DTI measures from 7 different brain pathways. To compare the efficacy of the classification for 3 animal groups, the linear regression analysis (LDA) and the machine learning technique (MLP) were applied. It was found that the testing accuracy by MLP was about 77%, but that of accuracy by LDA was much higher than MLP. In conclusion, machine learning algorithm could be used to identify and predict the changes of the brain white matter in some situations. The limits of this study will be discussed.

• 지역과문화
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• v.7 no.1
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• pp.183-208
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• 2020

Presenting symbolic concepts consistent with the culture of the host country and international trends at international events will win the favor of the world and raise the image of the host country. The international event mascot symbolically represents the host country's unique culture, and is a good means to enhance the sense of belonging and pride of its members and to display the image of the host country in an outwardly. This study discussed the symbolic elements of the host country characteristics reflected in FIFA's World Cup event mascot. A total of 14 mascots of World Cup events were held from 1966 to 2018, and their materials can be divided into animals, plants, people and creations. The mascot was applied with the characteristic elements of regional specialties, the flag of the host country, symbolic attire, language of the hosting area, social issues and the mascot's dress, posture, props and expression characters of soccer events were used as symbolic elements. First of all, the implications of the research were that mascots were more strongly expressing the "football" signifying element, the theme of events, rather than regional characteristics. Second, the use of 'national flag' was highlighted among the elements of expressing regional characteristics. Third, 'animal' was preferred for mascot material. Fourth, mascots have become integrated with 'cultural perfumes' and play an extended role in raising social awareness. Implications derived from the classification of characteristics and symbol representation elements raised in this study will be used as a basis for the planning of international event mascots.