• ETRI Journal
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• 제34권5호
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• pp.743-752
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• 2012

Semantic interpretation of the relationship between noun compound (NC) elements has been a challenging issue due to the lack of contextual information, the unbounded number of combinations, and the absence of a universally accepted system for the categorization. The current models require a huge corpus of data to extract contextual information, which limits their usage in many situations. In this paper, a new semantic relations interpreter for NCs based on novel lightweight binary features is proposed. Some of the binary features used are novel. In addition, the interpreter uses a new feature selection method. By developing these new features and techniques, the proposed method removes the need for any huge corpuses. Implementing this method using a modular and plugin-based framework, and by training it using the largest and the most current fine-grained data set, shows that the accuracy is better than that of previously reported upon methods that utilize large corpuses. This improvement in accuracy and the provision of superior efficiency is achieved not only by improving the old features with such techniques as semantic scattering and sense collocation, but also by using various novel features and classifier max entropy. That the accuracy of the max entropy classifier is higher compared to that of other classifiers, such as a support vector machine, a Na$\ddot{i}$ve Bayes, and a decision tree, is also shown.

• 한국컴퓨터정보학회논문지
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• 제21권9호
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• pp.11-18
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• 2016

In this paper, we propose a gas classification method using combined features for an electronic nose system that performs well even when some loss occurs in measuring data samples. We first divide the entire measurement for a data sample into three local sections, which are the stabilization, exposure, and purge; local features are then extracted from each section. Based on the discrimination analysis, measurements of the discriminative information amounts are taken. Subsequently, the local features that have a large amount of discriminative information are chosen to compose the combined features together with the global features that extracted from the entire measurement section of the data sample. The experimental results show that the combined features by the proposed method gives better classification performance for a variety of volatile organic compound data than the other feature types, especially when there is data loss.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권2호
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• pp.929-944
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• 2019

Many critical applications require accurate real-time human action recognition. However, there are many hurdles associated with capturing and pre-processing image data, calculating features, and classification because they consume significant resources for both storage and computation. To circumvent these hurdles, this paper presents a recognition machine learning (ML) based system model which uses reduced data structure features by projecting real 3D skeleton modality on virtual 2D space. The MMU VAAC dataset is used to test the proposed ML model. The results show a high accuracy rate of 97.88% which is only slightly lower than the accuracy when using the original 3D modality-based features but with a 75% reduction ratio from using RGB modality. These results motivate implementing the proposed recognition model on an embedded system platform in the future.

• Asian-Australasian Journal of Animal Sciences
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• 제33권4호
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• pp.539-546
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• 2020

Objective: The Jeju native pig (JNP) found on the Jeju Island of Korea is a unique black pig known for high-quality meat. To investigate the genetic uniqueness of JNP, we analyzed the selection signature of the JNP in comparison to commercial pigs such as Berkshire and Yorkshire pigs. Methods: We surveyed the genetic diversity to identify the genetic stability of the JNP, using the linkage disequilibrium method. A selective sweep of the JNP was performed to identify the selection signatures. To do so, the population differentiation measure, Weir-Cockerham's F_st was utilized. This statistic directly measures the population differentiation at the variant level. Additionally, we investigated the gene ontologies (GOs) and genetic features. Results: Compared to the Berkshire and Yorkshire pigs, the JNP had lower genetic diversity in terms of linkage disequilibrium decays. We summarized the selection signatures of the JNP as GO. In the JNP and Berkshire pigs, the most enriched GO terms were epithelium development and neuron-related. Considering the JNP and Yorkshire pigs, cellular response to oxygen-containing compound and generation of neurons were the most enriched GO. Conclusion: The selection signatures of the JNP were identified through the population differentiation statistic. The genes with possible selection signatures are expected to play a role in JNP's unique pork quality.

• 한국정보과학회논문지:컴퓨팅의 실제 및 레터
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• 제15권12호
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• pp.928-932
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• 2009

생체 내에서 질병 발생은 다양한 요인들의 복합적인 작용에 의해 발생한다. 하지만, 대부분의 질병 관련 원인을 발견하고자하는 연구들에서는 여러 요인들의 다양한 조합들을 복합적으로 고려하여 분석하기에는 한계가 있는 경우가 많다. 단 하나의 질병 관련 요인들을 찾는 것데 그치고 있다. 본 연구에서는 유전체 정보과 임상 정보를 이용하여 질병 분류 모델 기반 인자 조합들의 진화적 학습 방법을 제안한다. 이 방법을 이용하여 본 연구에서는 질병 관련 주요 인자를 찾고, 이를 시각화할 수 있는 시스템을 만드는 것을 목적으로 한다. 본 연구를 통해 정확도 높은 당뇨병 환자군 분류 모델을 만들고, 당뇨병 발생에 중요한 영향을 미치는 인자들의 조합을 찾을 수 있었다. 또한 생뭍학적인 분석을 통해 본 연구에서 찾은 인자들의 조합이 실제로도 당뇨병 발생에 영향을 미치는 인자가 될 수 있음을 확인하고, 특히 각 인자들이 하나씩 존재할 때보다. 조합으로 존재할 경우 당뇨병 발생 가능성이 높아질 수 있음을 확인할 수 있었다.